HK40058647B - Methods of identification, assessment, prevention and therapy of lung diseases and kits thereof - Google Patents

Description

Methods and reagent kits for identifying, assessing, preventing, and treating lung diseases

This application is a divisional application of the invention patent application PCT/US2010/027243, filed on March 12, 2010, with Chinese national phase application number 201080017220.5.

Background of the Invention

(a) Field of Invention

This invention relates to the detection, identification, assessment, prevention, diagnosis, and treatment of lung diseases using biomarkers and their kits. More specifically, this invention relates to the diagnosis of non-small cell lung cancer and reactive airway diseases by measuring and quantifying the expression levels of specific biomarkers. This invention also relates to the identification of biomarkers present in human serum or other biological fluids, where the presence of biomarkers at levels different from those found in normal populations suggests a pathology associated with human lung tissue and the human respiratory system. By identifying biomarkers associated with these pathological conditions, quantifying the expression levels of these biomarkers, and comparing the expression levels with those typically expected to be present in normal human serum, it is possible to detect the presence of a pathological condition early in its progression with a simple blood test, qualitatively characterize the progression of said pathological condition, and differentiate between pathological conditions.

(b) Description of related fields

Respiratory pathologies, such as asthma and lung cancer, affect millions of Americans. In fact, the American Lung Association reports that nearly 20 million Americans suffer from asthma. The American Cancer Society estimates that in 2007 alone, there were 229,400 new cases of respiratory cancer and 164,840 deaths from respiratory cancer. While the five-year survival rate for all cancer cases when detected and still localized is 46%, the five-year survival rate for lung cancer patients is only 13%. Correspondingly, only 16% of cancers are discovered before the disease spreads. Lung cancer is generally classified into two main types based on the pathological state of the cancer cells. Each type is named according to the type of cells that transform into cancer. Small cell lung cancer originates from small cells in human lung tissue, while non-small cell lung cancer generally includes all lung cancers that are not small cell types. Because the treatment for all non-small cell types is generally the same, non-small cell lung cancer is grouped together. Collectively, non-small cell lung cancer, or NSCLC, accounts for approximately 75% of all lung cancers.

A major factor contributing to the low survival rate of lung cancer patients is the difficulty in diagnosing it in its early stages. Current methods for diagnosing lung cancer or identifying its presence in a person are limited to performing X-rays, computed tomography (CT) scans, and similar lung tests to physically determine the presence or absence of a tumor. Therefore, lung cancer diagnosis is often only made in response to symptoms that have been present for some time, and only after the disease has been present in the person long enough to produce a physically detectable mass.

Similarly, current methods for detecting asthma generally involve testing after symptoms such as recurrent wheezing, coughing, and chest tightness have been present for some time. Current asthma testing methods are generally limited to pulmonary function tests such as respiratory capacity measurement tests or challenge tests. Furthermore, physicians often request these tests along with several other tests to rule out other pathological conditions or reactive airway diseases such as chronic obstructive pulmonary disease (COPD), bronchitis, pneumonia, and congestive heart failure.

There is no simple and reliable method in the field for diagnosing the pathological state of human lung tissue in its early stages. Furthermore, there are currently no available blood tests that can definitively diagnose the presence of a specific lung tissue pathology. Therefore, there is a need to develop a method to identify the presence of lung cancer in its early stages of disease progression. Similarly, there is a need to develop a method to diagnose asthma and non-small cell lung cancer at the earliest onset of symptoms, and to differentiate them from each other and from other lung diseases such as infections. Further, there is a need to identify specific proteins present in human blood that indicate the presence of non-small cell lung cancer and/or reactive airway disease when their relative expression intensity changes.

Invention Overview

This invention has identified a large number of biomarkers that can be used to qualitatively assess the physiological status of subjects with lung diseases such as non-small cell lung cancer or reactive airway disease. These biomarkers are shown in Tables 1-23.

Table 1A lists such biomarkers whose expression levels, when measured in individuals with one or more lung diseases, differ from those in healthy individuals. Table 1B lists such biomarkers whose expression levels, when measured in individuals with non-small cell lung cancer or reactive airway disease, differ from those in healthy individuals, and shows differential expression levels between non-small cell lung cancer and reactive airway disease. Table 1C lists such biomarkers whose expression, when measured, differs from those in healthy individuals.

Table 2 lists such biomarkers whose expression levels differ from those in normal individuals when measured in individuals with reactive airway disease. Table 3 lists such biomarkers whose expression levels differ from those in normal individuals when measured in individuals with non-small cell lung cancer. Table 4 lists such biomarkers whose expression levels differ between individuals with non-small cell lung cancer and reactive airway disease when measured.

Table 5A lists such biomarkers whose expression levels, when measured in men with one or more lung diseases, differ from those in normal men. Table 5B lists such biomarkers whose expression levels, when measured in men with non-small cell lung cancer or reactive airway disease, differ from those in normal men, and shows differential expression levels between non-small cell lung cancer and reactive airway disease. Table 5C lists such biomarkers whose expression levels, when measured in men with both non-small cell lung cancer and reactive airway disease, differ from those in normal men.

Table 6 lists such biomarkers whose expression levels differed from those in normal men when measured in men with reactive airway disease. Table 7 lists such biomarkers whose expression levels differed from those in normal men when measured in men with non-small cell lung cancer. Table 8 lists such biomarkers whose expression levels differed between men with non-small cell lung cancer and men with reactive airway disease when measured.

Table 9A lists such biomarkers whose expression levels, when measured in women with one or more lung diseases, differ from those in normal women. Table 9B lists such biomarkers whose expression levels, when measured in women with non-small cell lung cancer or reactive airway disease, differ from those in normal women, and shows differential expression levels between non-small cell lung cancer and reactive airway disease. Table 9C lists such biomarkers whose expression levels, when measured in women with both non-small cell lung cancer and reactive airway disease, differ from those in normal women.

Table 10 lists such biomarkers that have been found to be expressed differently in women with reactive airway disease than in normal women when measured. Table 11 lists such biomarkers that have been found to be expressed differently in women with non-small cell lung cancer than in normal women when measured. Table 12 lists such biomarkers that have been found to have different expression levels when measured between women with non-small cell lung cancer or reactive airway disease.

Table 13A lists biomarkers whose expression differs significantly between male and female reactive airway disease populations. Table 13B lists biomarkers whose expression does not differ significantly between male and female reactive airway disease populations. Table 14A lists biomarkers whose expression differs significantly between male and female non-small cell lung cancer populations. Table 14B lists biomarkers whose expression does not differ significantly between male and female non-small cell lung cancer populations. Table 15A lists biomarkers ranked by the relative standard deviation of fluorescence intensity in a normal population. Table 15B lists biomarkers ranked by the relative standard deviation of fluorescence intensity in a normal female population. Table 15C lists biomarkers ranked by the relative standard deviation of fluorescence intensity in a normal male population.

Table 16A lists such biomarkers whose expression levels, when measured in men with one or more lung diseases, differ from those in normal men. Table 16B lists such biomarkers whose expression levels, when measured in men with non-small cell lung cancer or reactive airway disease, differ from those in normal men, and shows differential expression levels between non-small cell lung cancer and reactive airway disease. Table 16C lists such biomarkers whose expression levels, when measured in men with both non-small cell lung cancer and reactive airway disease, differ from those in normal men.

Table 17 lists such biomarkers whose expression levels differed from those in normal men when measured in men with reactive airway disease. Table 18 lists such biomarkers whose expression levels differed from those in normal men when measured in men with non-small cell lung cancer. Table 19 lists such biomarkers whose expression levels differed between men with non-small cell lung cancer and men with reactive airway disease when measured.

Table 20A lists such biomarkers whose expression levels, when measured in women with one or more lung diseases, differ from those in normal women. Table 20B lists such biomarkers whose expression levels, when measured in women with non-small cell lung cancer or reactive airway disease, differ from those in normal women, and shows differential expression levels between non-small cell lung cancer and reactive airway disease. Table 20C lists such biomarkers whose expression levels, when measured in women with non-small cell lung cancer and reactive airway disease, differ from those in normal women.

Table 21 lists such biomarkers that have been found to have different expression levels in women with reactive airway disease compared to normal women when measured. Table 22 lists such biomarkers that have been found to have different expression levels in women with non-small cell lung cancer compared to normal women when measured. Table 23 lists such biomarkers that have been found to have different expression levels between women with non-small cell lung cancer or reactive airway disease when measured.

The significance of Tables 1-15 was determined using the Student’s t-test. The significance of Tables 16-23 was determined using the Kruskal-Wallis method.

The peptides containing SEQ ID NO: 1-17 are additional biomarkers whose expression has been found to change according to one or more lung diseases.

This invention provides a variety of diagnostic, predictive, and therapeutic methods that rely on the identification of these biomarkers.

This invention provides a method for physiological characterization in subjects, comprising determining the expression level of at least one biomarker from any number in Tables 1-12 or 16-23 in a physiological sample of the subject, wherein the expression level of the at least one biomarker indicates or helps differentiate from lung diseases such as non-small cell lung cancer or reactive airway disease. This invention also provides a method for physiological characterization in subjects, comprising determining the expression level of at least one biomarker from Tables 13B, 14B, or 15B and also appearing in Tables 1-12 or 16-23 in a physiological sample of the subject, preferably the biomarker being at least one of biomarkers numbered 1-10 in Tables 1-12 or 16-23, wherein the expression level of the at least one biomarker indicates from lung diseases such as non-small cell lung cancer or reactive airway disease. Alternatively, or additionally, the expression level of first-level interactors of these biomarkers may be determined.

The present invention provides a method for performing physiological qualitative analysis in a subject, including determining the expression level of SEQ ID NO:12 in a physiological sample of the subject, wherein the expression level of SEQ ID NO:12 indicates lung disease such as non-small cell lung cancer or reactive airway disease.

The present invention provides a method for physiological qualitative analysis in a subject, comprising determining the expression level of at least one polypeptide selected from SEQ ID NO: 1-17 in a physiological sample of the subject, and determining the expression level of at least one biomarker from any number in Tables 1-12 or 16-23, wherein the at least one polypeptide and the at least one biomarker from any number in Tables 1-12 or 16-23 indicate lung diseases such as non-small cell lung cancer or reactive airway disease.

The present invention provides a method for diagnosing reactive airway disease in subjects, comprising determining the expression level of at least one biomarker from Tables 2, 6, 10, 17 and 21 in a physiological sample of the subject, wherein the expression level of the at least one biomarker indicates reactive airway disease.

The present invention provides a method for diagnosing non-small cell lung cancer in subjects, comprising determining the expression level of at least one biomarker from Table 3, Table 7, Table 11, Table 18 or Table 22 in a physiological sample of the subject, wherein the expression level of the at least one biomarker indicates non-small cell lung cancer.

This invention provides a diagnostic method to aid in distinguishing the likelihood of a subject having a risk of non-small cell lung cancer or reactive airway disease, comprising determining the expression level of at least one biomarker from Tables 4, 8, 12, 19, or 23 in a physiological sample of a subject at risk of at least one of non-small cell lung cancer or reactive airway disease, wherein the expression level of the at least one biomarker from Tables 4, 8, 12, 19, or 23 aids in distinguishing the likelihood of the subject having a risk of non-small cell lung cancer or reactive airway disease.

The present invention provides a method for predicting the likelihood that a patient will respond to a therapeutic intervention, comprising determining the expression level of at least one biomarker described herein in a physiological sample of a subject, wherein the expression level of the at least one biomarker assists in predicting the subject's response to the therapeutic intervention.

The present invention also provides a method for monitoring subjects, comprising determining the first expression level of at least one biomarker described herein in a physiological sample of the subject, determining the second expression level of said at least one biomarker in the physiological sample of the subject at a subsequent time after the first determination, and comparing said first expression level with said second expression level.

The present invention also provides a method for designing a kit, comprising selecting at least one biomarker described herein, selecting a means for determining the expression level of said at least one biomarker, and designing a kit comprising said means for determining the expression level.

The present invention also provides a method for designing a kit, comprising selecting at least one biomarker described herein, selecting a detection reagent for detecting said at least one biomarker, and designing a kit comprising said detection reagent for detecting said at least one biomarker.

The present invention also provides a kit comprising at least one biomarker described herein.

The present invention also provides a kit comprising tools for determining the expression level of at least one polypeptide selected from SEQ ID NO: 12.

The present invention also provides a kit comprising a detection reagent for detecting at least one polypeptide selected from SEQ ID NO: 12.

The present invention also provides a kit comprising (a) a tool for determining the expression level of at least one polypeptide selected from SEQ ID NO: 1-17, and (b) a tool for determining the expression level of at least one biomarker from any of Tables 1-12 or Tables 16-23.

The present invention also provides a kit comprising (a) a detection reagent for detecting at least one polypeptide selected from SEQ ID NO: 1-17, and (b) a detection reagent for detecting at least one biomarker from any of Tables 1-12 or Tables 16-23.

The present invention further provides a kit containing biomarkers and/or peptides from the above tables.

Brief description of the attached diagram

Figure 1A shows the mean fluorescence intensity levels of biomarkers, as well as the standard deviation and relative standard deviation, in the normal (NO) population from Example 1.

Figure 1B shows the mean fluorescence intensity levels of biomarkers, as well as the standard deviation and relative standard deviation, in the non-small cell lung cancer (LC) population from Example 1.

Figure 1C shows the mean fluorescence intensity levels of biomarkers, as well as the standard deviation and relative standard deviation, in the asthma (AST) population from Example 1.

Figure 1D shows the percentage change in the average fluorescence intensity of each biomarker from Example 1 relative to the NO population, the AST population relative to the NO population, and the LC population relative to the AST population.

Figure 1E shows the probabilities associated with the Student's t-value obtained by comparing the mean fluorescence intensity and variance measured for each biomarker in the populations from Example 1, where the populations to be compared are the LC population relative to the NO population, the AST population relative to the NO population, and the LC population relative to the AST population.

Figure 2A shows the mean fluorescence intensity levels of biomarkers, as well as the standard deviation and relative standard deviation, in the normal (NO) population from Example 2.

Figure 2B shows the mean fluorescence intensity levels of biomarkers, as well as the standard deviation and relative standard deviation, in the non-small cell lung cancer (LC) population from Example 2.

Figure 2C shows the mean fluorescence intensity levels of biomarkers, as well as the standard deviation and relative standard deviation, in the asthma (AST) population from Example 2.

Figure 2D shows the percentage change in the average fluorescence intensity of each biomarker from Example 2 relative to the LC population, the AST population relative to the NO population, and the AST population relative to the LC population.

Figure 2E shows the probabilities associated with the Student's t-value obtained by comparing the mean fluorescence intensity and variance measured for each biomarker in the populations from Example 2, where the populations to be compared are the LC population relative to the NO population, the AST population relative to the NO population, and the AST population relative to the LC population.

Figure 3A shows the mean fluorescence intensity levels of biomarkers, as well as the standard deviation and relative standard deviation, in the normal (NO) population from Example 3.

Figure 3B shows the mean fluorescence intensity levels of biomarkers, as well as the standard deviation and relative standard deviation, in the non-small cell lung cancer (LC) population from Example 3.

Figure 3C shows the mean fluorescence intensity levels of biomarkers, as well as the standard deviation and relative standard deviation, in the asthma (AST) population from Example 3.

Figure 3D shows the percentage change in the average fluorescence intensity of each biomarker from Example 3 relative to the NO population, the LC population relative to the NO population, and the AST population relative to the LC population.

Figure 3E shows the probabilities associated with the Student's t-value obtained by comparing the mean fluorescence intensity and variance measured for each biomarker in the populations from Example 3, where the populations to be compared are the AST population relative to the NO population, the LC population relative to the NO population, and the AST population relative to the LC population.

Figure 4A shows the mean fluorescence intensity levels of biomarkers, as well as the standard deviation and relative standard deviation, in the normal (NO) female population from Example 3.

Figure 4B shows the mean fluorescence intensity levels of biomarkers, as well as the standard deviation and relative standard deviation, in the female non-small cell lung cancer (LC) population from Example 3.

Figure 4C shows the mean fluorescence intensity levels of biomarkers, as well as the standard deviation and relative standard deviation, in the female asthma (AST) cohort from Example 3.

Figure 4D shows the percentage change in the average fluorescence intensity of each biomarker from Example 3 relative to the NO female population, the LC female population relative to the NO female population, and the AST female population relative to the LC female population.

Figure 4E shows the probabilities associated with the Student's t-value obtained by comparing the mean fluorescence intensity and variance measured for each biomarker in the populations from Example 3, where the populations to be compared are the AST population relative to the NO female population, the LC population relative to the NO female population, and the AST population relative to the LC female population.

Figure 5A shows the mean fluorescence intensity levels of biomarkers, as well as the standard deviation and relative standard deviation, in the normal (NO) male population from Example 3.

Figure 5B shows the mean fluorescence intensity levels of biomarkers, as well as the standard deviation and relative standard deviation, in the male non-small cell lung cancer (LC) population from Example 3.

Figure 5C shows the mean fluorescence intensity levels of biomarkers, as well as the standard deviation and relative standard deviation, in the male asthma (AST) population from Example 3.

Figure 5D shows the percentage change in the average fluorescence intensity of each biomarker from Example 3 relative to the NO male population, the LC population relative to the NO male population, and the AST population relative to the LC male population.

Figure 5E shows the probabilities associated with the Student's t-value obtained by comparing the mean fluorescence intensity and variance measured for each biomarker in the populations from Example 3, where the populations to be compared are AST relative to NO males, LC relative to NO males, and LC relative to AST males.

Figure 6A shows the percentage change in the average fluorescence intensity of each biomarker from Example 3 compared to the AST female population, the LC male population compared to the LC female population, and the NO male population compared to the NO female population.

Figure 6B shows the probabilities associated with the Student's t-value obtained by comparing the mean fluorescence intensity and differences measured for each biomarker from male and female populations in Example 3, where the populations to be compared are the AST male and female population, the LC male and female population, and the NO male and female population.

Figure 7A shows the percentage change in the average concentration of each biomarker from the LC relative to NO female population, AST relative to NO female population, and AST relative to LC female population in Example 3.

Figure 7B shows the probabilities of the Kruskal-Wallis test correlation calculated by comparing the concentrations of each biomarker measured against the female populations from Example 3, where the populations to be compared are the AST vs. NO female population, the LC vs. NO female population, and the AST vs. LC female population.

Figure 8A shows the percentage change in the average concentration of each biomarker from the LC relative to NO male population, AST relative to NO male population, and AST relative to LC male population in Example 3.

Figure 8B shows the probabilities of the Kruskal-Wallis test correlation calculated by comparing the concentrations of each biomarker measured against the male populations from Example 3, where the populations to be compared are the AST-NO male population, the LC-NO male population, and the AST-LC male population.

Figure 9 shows the relationships between the biomarkers in Table 16B.

Invention Details

This invention relates to various methods for the detection, identification, assessment, prevention, diagnosis, and treatment of lung diseases using biomarkers, including gender-based detection, identification, assessment, prevention, diagnosis, and treatment of diseases. These methods involve determining the expression levels of specific biomarkers, the altered expression of which indicates non-small cell lung cancer and/or reactive airway diseases (e.g., asthma, chronic obstructive pulmonary disease, etc.). The invention also provides various kits comprising diagnostic reagents for detecting these biomarkers or tools for determining the expression levels of these biomarkers.

As used herein, a “biomarker” or “marker” is a biological molecule that is objectively measured to serve as a characteristic indicator of the physiological state of a biological system. For the purposes of this disclosure, biological molecules include ions, small molecules, peptides, proteins and peptides and proteins with post-translational modifications, nucleosides, nucleotides and polynucleotides including RNA and DNA, glycoproteins, lipoproteins, and various covalent or non-covalent modifications of these types of molecules. Biological molecules include any kind of entities that are native to, characteristic of, and/or essential to the function of a biological system. Most biomarkers are polypeptides, although they may also be pre-translational mRNA or modified mRNA representing a gene product expressed as a polypeptide, or may include post-translational modifications of said polypeptide.

As used herein, “subject” refers to any animal, but preferably a mammal, such as a human. In many embodiments, the subject is a human patient who has lung disease or is at risk of developing lung disease.

As used herein, “physiological sample” includes samples from biological fluids and tissues. Biological fluids include whole blood, plasma, serum, sputum, urine, sweat, lymph, and bronchoalveolar lavage fluid. Tissue samples include biopsies from solid lung tissue or other solid tissues, lymph node biopsies, and metastatic lesion biopsies. Methods for obtaining physiological samples are well known.

As used in this article, “therapeutic intervention” includes the application of one or more therapeutic agents such as small or large molecules, radiation, surgery, or a combination thereof.

As used herein, “detection reagent” includes reagents and systems for the specific detection of the biomarkers described herein. Detection reagents include reagents such as antibodies, nucleic acid probes, aptamers, lectins, or other reagents that have a specific affinity for one or more specific markers sufficient to distinguish the specific marker from other markers that may be present in the target sample, and systems such as sensors, including sensors that utilize bound or immobilized ligands as described above.

Identification of biomarkers

The biomarkers of the present invention are identified using two methods. First, biomarkers indicative of non-small cell lung cancer and/or asthma are identified by comparing the expression levels of 59 selected biomarkers measured in the plasma of patients from a group diagnosed with distinct pathological conditions with those from a group not diagnosed with said pathological conditions (confirmed by a physician). This method is detailed in Examples 1-3.

The second method uses mass spectrometry to identify biomarkers. Proteins indicative of non-small cell lung cancer and/or asthma are identified by comparing mass spectrometry data of trypsin peptide digestion from samples obtained from patients in different physiological states. Specifically, the data is the mass of peptide fragments, a graphical display of the intensity of pseudo- or protonated molecular ion signals of peptides and proteins containing these fragments expressed over a period of time in a single dimension. Comparison of expression levels of thousands of proteins resulted in the identification of 17 proteins with significantly different expression intensities among a group of individuals without any diagnosed lung histopathology, a group of individuals with asthma (diagnosed by a physician), and a group of individuals with non-small cell lung cancer (diagnosed by a physician). This method is detailed in Examples 6 and 7.

First-order interactors

Biological molecules must interact to promote and control a variety of cellular and organ physiological functions essential for maintaining life. These interactions can be considered a type of communication. In this communication, various biological molecules are considered information. As essential components of their signal transduction functions, these molecules must interact with a wide range of targets, including other types of biological molecules.

One type of interacting molecule is commonly called a receptor. Another type of direct intermolecular interaction is the binding of cofactors to enzymes. These intermolecular interactions form a network of signaling molecules that work together to perform and control key life functions of cells and organs. The specific biomarkers of this invention are physiologically linked to other biomarkers, whose levels increase or decrease in a synergistic manner with the levels of the specific biomarkers. These other biomarkers are called "first-order interactors" relative to the specific biomarkers of this invention.

"First-level interactors" are molecular entities that directly interact with specific biological molecules. For example, the drug morphine directly interacts with opiate receptors, ultimately leading to pain relief. Therefore, under the definition of "first-level interactors," opiate receptors are first-level interactors. First-level interactors include the direct upstream and downstream neighbors of the biomarkers in the communication pathways in which they interact. These entities include proteins, nucleic acids, and small molecules that can be linked through various relationships, including but not limited to direct (or indirect) regulation, expression, chemical reactions, molecular synthesis, binding, promoter binding, protein modification, and molecular transport. The group of biomarkers that exhibit horizontal synergy is well known to those skilled in the art, as well as to physiologists and cell biologists. In fact, first-level interactors for specific biomarkers are known in the art and can be found using various databases and available bioinformatics software such as ARIADNE PATHWAY STUDIO, ExPASY Proteins Server Qlucore Omics Explorer, Protein Prospector, PQuad, ChEMBL, and others. (See, for example, ARIADNE PATHWAY STUDIO, Ariadne, Inc., <www.ariadne.genomics.com> or ChEMBL Database, European Bioinformatics Institute, European Molecular Biology Laboratory, <www.ebi.ac.uk>).

When the level of a specific biomarker of the present invention is abnormal, the level of a first-level interactor biomarker expressing synergy with the specific biomarker is also abnormal. Therefore, determining that the level of a specific biomarker is abnormal can be accomplished by measuring the level of the expected synergistic first-level interactor. Those skilled in the art will naturally recognize that the level of the first-level interactor used to replace or be added to a specific biomarker will vary in a deterministic and reproducible manner consistent with the behavior of the specific biomarker.

This invention provides for any method described herein that, alternatively, can be performed using the first-order interactors of a specific biomarker. For example, this invention provides a method for physiological qualitative analysis, including determining the expression level of HGF. Thus, this invention also provides a method for physiological qualitative analysis, including determining the expression level of the first-order interactors of HGF. The first-order interactors of HGF include, but are not limited to, those identified in Example 12.

Table of significant biomarkers

Table 1A lists biomarkers whose expression levels showed significant or marginally significant differences among at least one of the AST vs. NO, LC vs. NO, and AST vs. LC populations. Significance was determined using Student's t-test as shown in Examples 1-3. Biomarkers were listed in descending order based on the significance and order of fluorescence intensity differences.

Table 1B lists biomarkers whose expression levels differed significantly between the AST/NO, LC/NO, and AST/LC populations. Significance was determined using Student's t-test as shown in Examples 1-3. Marginally significant biomarkers were excluded. Biomarkers are listed in descending order based on the order of fluorescence intensity difference.

Table 1C lists biomarkers whose expression levels showed significant or marginally significant differences between the AST and NO populations and between the LC and NO populations. Significance was determined using Student's t-test as shown in Examples 1-3. Biomarkers are listed in descending order based on the order of fluorescence intensity difference.

Table 2 lists biomarkers whose expression levels showed significant or marginally significant differences between the AST and NO populations. Significance was determined using Student's t-test as shown in Examples 1-3. Biomarkers are listed in descending order based on the order of fluorescence intensity difference.

Table 3 lists biomarkers whose expression levels showed significant or marginally significant differences between the LC and NO populations. Significance was determined using Student's t-test as shown in Examples 1-3. Biomarkers are listed in descending order based on the order of fluorescence intensity difference.

Table 4 lists biomarkers whose expression levels showed significant or marginally significant differences between the AST and LC populations. Significance was determined using Student's t-test as shown in Examples 1-3. Biomarkers were listed in descending order based on the order of fluorescence intensity difference.

Table 5A lists biomarkers whose expression levels showed significant or marginally significant differences among at least one of the AST-NO male population, LC-NO male population, and AST-LC male population. Significance was determined using Student's t-test as shown in Examples 1-3. Biomarkers were listed in descending order based on the significance and order of fluorescence intensity differences.

Table 5B lists biomarkers whose expression levels differed significantly between the AST and NO male populations, the LC and NO male populations, and the AST and LC male populations. Significance was determined using Student's t-test as shown in Examples 1-3. Marginally significant biomarkers were not included. Biomarkers are listed in descending order based on the order of fluorescence intensity difference.

Table 5C lists biomarkers whose expression levels showed significant or marginally significant differences between the AST and NO male populations, and between the LC and NO male populations. Significance was determined using Student's t-test as shown in Examples 1-3. Biomarkers are listed in descending order based on the order of fluorescence intensity difference.

Table 6 lists biomarkers whose expression levels showed significant or marginally significant differences between the male populations with AST and NO. Significance was determined using Student's t-test as shown in Examples 1-3. The biomarkers are listed in descending order based on the order of fluorescence intensity difference.

Table 7 lists biomarkers whose expression levels showed significant or marginally significant differences between the LC and NO male populations. Significance was determined using Student's t-test as shown in Examples 1-3. Biomarkers were listed in descending order based on the order of fluorescence intensity differences.

Table 8 lists biomarkers whose expression levels showed significant or marginally significant differences between the AST and LC male populations. Significance was determined using Student's t-test as shown in Examples 1-3. Biomarkers are listed in descending order based on the order of fluorescence intensity difference.

Table 9A lists biomarkers whose expression levels showed significant or marginally significant differences among at least one of the AST-NO female population, LC-NO female population, and AST-LC female population. Significance was determined using Student's t-test as shown in Examples 1-3. Biomarkers were listed in descending order based on the significance and order of fluorescence intensity differences.

Table 9B lists biomarkers whose expression levels showed significant differences among the AST-NO female population, the LC-NO female population, and the AST-LC female population. Significance was determined using Student's t-test as shown in Examples 1-3. Marginally significant biomarkers were not included. Biomarkers are listed in descending order based on the order of fluorescence intensity differences.

Table 9C lists biomarkers whose expression levels showed significant or marginally significant differences between the AST and NO female populations and between the LC and NO female populations. Significance was determined using Student's t-test as shown in Examples 1-3. Biomarkers are listed in descending order based on the order of fluorescence intensity difference.

Table 10 lists the significant or marginally significant differences in expression levels between the female AST and NO groups. Significance was determined using Student's t-test as shown in Examples 1-3. The markers were listed in descending order based on the order of fluorescence intensity differences.

Table 11 lists biomarkers whose expression levels showed significant or marginally significant differences between the LC and NO female populations. Significance was determined using Student's t-test as shown in Examples 1-3. Biomarkers were listed in descending order based on the order of fluorescence intensity differences.

Table 12 lists biomarkers whose expression levels showed significant or marginally significant differences between the AST and LC female populations. Significance was determined using Student's t-test as shown in Examples 1-3. Biomarkers are listed in descending order based on the order of fluorescence intensity difference.

Table 13A lists biomarkers whose expression levels differed significantly or marginally significantly between male and female AST populations. Significance was determined using Student's t-test as shown in Examples 1-3. The biomarkers are listed in descending order based on the order of fluorescence intensity difference.

Table 13B lists biomarkers whose expression levels did not differ significantly between male and female AST populations. Significance was determined using Student's t-test as shown in Examples 1-3. The biomarkers are listed in descending order based on the order of difference in fluorescence intensity.

Table 14A lists biomarkers whose expression levels differed significantly or marginally significantly between male and female LC populations. Significance was determined using Student's t-test as shown in Examples 1-3. The biomarkers are listed in descending order based on the order of fluorescence intensity difference.

Table 14B lists biomarkers whose expression levels did not differ significantly between male and female LC populations. Significance was determined using Student's t-test as shown in Examples 1-3. The biomarkers were listed in descending order based on the significance and order of differences in fluorescence intensity.

Table 15A lists the following biomarkers, ordered in descending order of relative standard deviation of fluorescence intensity in the normal population.

Table 15B lists the following biomarkers, ordered in descending order of relative standard deviation of fluorescence intensity in a normal female population.

Table 15C lists the following biomarkers, ordered in descending order of relative standard deviation of fluorescence intensity in a normal male population.

Table 16A lists biomarkers whose expression levels are significantly different among at least one of the AST-NO male population, LC-NO male population, and AST-LC male population. Significance was determined using the Kruskal-Wallis method, as shown in Example 4. Marginally significant biomarkers are not included. Biomarkers are listed in descending order based on the significance and order of fluorescence intensity differences.

Table 16B lists biomarkers whose expression levels differed significantly between the AST and NO male populations, the LC and NO male populations, and the AST and LC male populations. Significance was determined using the Kruskal-Wallis method, as shown in Example 4. Marginally significant biomarkers were not included. Biomarkers were listed in descending order based on the degree of difference in fluorescence intensity.

Table 16C lists biomarkers whose expression levels differed significantly between the AST and NO male populations and between the LC and NO male populations. Significance was determined using the Kruskal-Wallis method, as shown in Example 4. Marginally significant biomarkers are not included. Biomarkers were deduced based on the significance and order of fluorescence intensity differences.

Table 17 lists biomarkers whose expression levels differed significantly between the male populations with AST and NO. Significance was determined using the Kruskal-Wallis method, as shown in Example 4. Marginally significant biomarkers were not included. Biomarkers were listed in descending order based on the order of fluorescence intensity difference.

Table 18 lists biomarkers whose expression levels differed significantly between the LC and NO male populations. Significance was determined using the Kruskal-Wallis method, as shown in Example 4. Marginally significant biomarkers were not included. Biomarkers were listed in descending order based on the order of fluorescence intensity difference.

Table 19 lists biomarkers whose expression levels differed significantly between the AST and LC male populations. Significance was determined using the Kruskal-Wallis method, as shown in Example 4. Marginally significant biomarkers were not included. Biomarkers were listed in descending order based on the order of fluorescence intensity difference.

Table 20A lists biomarkers whose expression levels are significantly different among at least one of the AST-NO female population, LC-NO female population, and AST-LC female population. Significance was determined using the Kruskal-Wallis method, as shown in Example 4. Marginally significant biomarkers are not included. Biomarkers are deduced based on the significance and order of fluorescence intensity differences.

Table 20B lists biomarkers whose expression levels differed significantly between the AST-NO female population, the LC-NO female population, and the AST-LC female population. Significance was determined using the Kruskal-Wallis method, as shown in Example 4. Marginally significant biomarkers were not included. Biomarkers were listed in descending order based on the degree of difference in fluorescence intensity.

Table 20C lists biomarkers whose expression levels differed significantly between the AST and NO female populations and between the LC and NO female populations. Significance was determined using the Kruskal-Wallis method, as shown in Example 4. Marginally significant biomarkers are not included. Biomarkers are listed in descending order based on the order of fluorescence intensity difference.

Table 21 lists biomarkers whose expression levels differed significantly between the female populations of AST and NO. Significance was determined using the Kruskal-Wallis method, as shown in Example 4. Marginally significant biomarkers were excluded. Biomarkers were listed in descending order based on the order of fluorescence intensity difference.

Table 22 lists biomarkers whose expression levels differed significantly between the LC and NO female populations. Significance was determined using the Kruskal-Wallis method, as shown in Example 4. Marginally significant biomarkers were excluded. Biomarkers were listed in descending order based on the order of fluorescence intensity difference.

Table 23 lists biomarkers whose expression levels differed significantly between the AST and LC female populations. Significance was determined using the Kruskal-Wallis method, as shown in Example 4. Marginally significant biomarkers were excluded. Biomarkers were listed in descending order based on the order of fluorescence intensity difference.

Determine the degree of expression

Expression levels typically involve quantitative measurements of the expressed product (generally a protein or peptide). This invention includes determining the expression levels of RNA (pre-translational) or proteins (which may include post-translational modifications). Specifically, this invention includes determining changes in biomarker concentrations that reflect increases or decreases in the levels of transcription, translation, post-translational modifications, or the degree or extent of protein degradation, wherein these changes are associated with a specific disease state or disease progression.

Samples are collected to ensure that the expression level in the subjects is proportional to the concentration of the biomarker in the sample. Measurements are performed to ensure that the measured value is proportional to the concentration of the biomarker in the sample. Therefore, the measured value is proportional to the expression level. Sampling and measurement techniques suitable for these requirements are selected within the scope of the art.

Generally, the expression level of at least one biomarker indicating lung disease is the level of at least one biomarker that differs statistically significantly from the mean expression level in normal individuals; in other words, at least one biomarker shows a statistically significant deviation from normal. Statistical significance and deviation can be determined using any known method for comparing population means or comparing measurements and means of populations. These methods include Student's t-tests for single or multiple biomarkers considered together, analysis of variance (ANOVA), etc.

As an alternative or combined approach to determining the level of expression, the methods described herein include determining whether the level of a biomarker is within or outside the normal range (i.e., abnormal). Those who measure the levels of biological molecules in physiological samples routinely determine the normal levels of specific biomarkers in their routinely measured populations, which are generally described as the normal range of values determined by the specific laboratory. Therefore, technicians will inevitably be familiar with the normal levels of specific biomarkers and be able to determine whether biomarker levels are outside the normal range or outside the normal range.

More generally, the expression level of at least one biomarker indicating lung disease is a level of at least one biomarker with a difference sufficient to be analytically significant compared to the mean expression level in normal individuals, thus enabling the diagnosis, prediction, and/or assessment of lung disease to be determined. Those skilled in the art will recognize that a larger difference in magnitude is preferred in aiding the diagnosis, prediction, and/or assessment of lung disease. See Instrumental Methods of Analysis, Seventh Edition, 1988.

Many proteins expressed in normal subjects are also expressed to varying degrees in subjects with diseases or conditions, such as non-small cell lung cancer or asthma. Those skilled in the art will recognize that most diseases exhibit changes in multiple, different biomarkers. Thus, diseases can be characterized by the expression patterns of multiple biomarkers. In fact, the expression patterns of multiple biomarkers can be used in a variety of diagnostic and predictive methods, as well as monitoring, treatment selection, and patient assessment methods. This invention provides such methods. These methods include determining the expression patterns of multiple biomarkers for a specific physiological state, or identifying changes in such patterns that are associated with changes in the physiological state, which is characterized by any technique used for appropriate pattern recognition.

Numerous methods for determining expression levels are known in the art. Methods for determining expression include, but are not limited to, radioimmunoassays, enzyme-linked immunosorbent assays (ELISA), high-performance liquid chromatography (HPLC) or mass spectrometry using radiometric or spectral measurements of absorption by visible or ultraviolet light for qualitative and quantitative analysis, Western blotting, 1- or 2-dimensional gel electrophoresis (which provides quantitative imaging via radioactive, fluorescent, or chemiluminescent probes or nuclei), antibody-based absorption or fluorescence spectrophotometry, luminescent quantification of any species in a large number of chemiluminescent reporter subsystems, enzymatic assays, immunoprecipitation or immunocapture assays, solid-phase and liquid-phase immunoassays, protein arrays or chips, DNA arrays or chips, plate assays, assays using molecules with binding affinity (which allow for differentiation such as aptamers and molecularly imprinted polymers), and any other quantitative analysis of biomarker concentrations using any other suitable technique, any of the described detection techniques, or the instrumental action of the instrument.

The step of determining the level of expression can be performed by any method known in the art for determining expression, especially those discussed herein. In a preferred embodiment, the step of determining the level of expression includes an immunoassay using antibodies.

Selecting biomarkers for determination

Those skilled in the art can readily select appropriate antibodies for use in this invention. The selected antibody is preferably selective for the target antigen, has high binding specificity to the antigen, and minimal cross-reactivity with other antigens. The ability of the antibody to bind to the target antigen can be determined, for example, by known methods such as enzyme-linked immunosorbent assay (ELISA), flow cytometry, and immunohistochemistry. Preferably, the target antigen bound by the antibody is differentially expressed in cells or biological samples taken from diseased patients relative to cells or biological samples taken from healthy patients. The differential expression of the antigen in different populations can be determined by comparing the binding of the antibody to samples taken from various target populations (e.g., diseased populations relative to healthy populations). See, for example, Examples 1-4; also see Figures 1-8. For example, it can be determined that the target antigen is expressed at a higher level in cancer cells than in non-cancer cells. See, for example, Examples 1-4; also see Figures 1-8. Further, the antibody should have relatively high binding specificity to the target antigen. The binding specificity of the antibody can be determined by known methods such as immunoprecipitation or by in vitro binding assays such as radioimmunoassay (RIA) or ELISA. A method for selecting antibodies capable of binding to a target antigen with high binding specificity and minimal cross-reactivity is disclosed, for example, U.S. Patent No. 7,288,249, the entire contents of which are incorporated herein by reference.

This invention provides various methods comprising the step of determining the expression levels of one or more biomarkers described herein. In one embodiment, the method includes determining the expression level of any biomarker from any of the numbers in Tables 1-14 or 16-23. The biomarkers in Tables 1-14 and 16-23 are generally listed in descending order of expression levels. Biomarkers closer to the top of these tables show higher sensitivity (e.g., detecting differences at lower levels). Using these biomarkers can aid in differentiating disease conditions. The biomarkers in Table 15 are listed in descending order based on the relative standard deviation of fluorescence intensity. Biomarkers closer to the top of Table 15 are generally also more sensitive due to the lower variance rather than variance resulting from the presence of a disease state. Specifically, these biomarkers have less overall variability, thus helping to reduce background noise when comparing the expression levels of diseased individuals with those of healthy individuals.

Thus, a preferred method includes determining the expression levels of biomarkers 1-20 in a specific table (or all listed biomarkers when the table contains fewer than 20). Alternatively, this mode includes determining the expression levels of biomarkers 1-10, more preferably 1-8, even more preferably 1-6, most preferably 1-4, or subgroups of biomarkers within any of these groups. In another embodiment, the method includes determining the expression levels of any combination of biomarkers from a specific table. In another embodiment, the method includes determining the expression levels of any combination of multiple biomarkers from biomarkers 1-20 in a specific table (or, when less than 20, the most listed biomarkers), preferably any combination of multiple biomarkers from biomarkers 1-10, more preferably any combination of multiple biomarkers from biomarkers 1-8, even more preferably any combination of biomarkers from biomarkers 1-6, most preferably any combination of multiple biomarkers from biomarkers 1-4, or a subgroup of biomarkers from any of these groups. In a preferred mode, the method includes determining the expression levels of any specific subgroup of three biomarkers selected from biomarkers 1-6, 1-8, 1-10, 1-15, or 1-20 in a specific table. Alternatively, the method includes determining the expression levels of any specific subgroup of four, five, six, or seven biomarkers selected from biomarkers 1-8, 1-10, 1-15, or 1-20 in a specific table. Alternatively, the method includes determining the expression levels of any specific subgroup of biomarkers selected from biomarkers 1-15 or 1-20 in the specific table. Of course, those skilled in the art will recognize that simultaneously determining the expression levels of other biomarkers, whether related to or unrelated to the target disease, is within the scope of this invention.

The determination of expression levels of multiple biomarkers allows for the observation of patterns of expression variation, which provide a more sensitive and accurate diagnosis than detecting individual biomarkers. For example, a pattern of variation may include multiple specific biomarkers expressed simultaneously at abnormal levels. A pattern of variation may also include abnormal increases in some specific biomarkers while abnormal decreases occur in others. Those skilled in the art will observe these patterns in the data presented in the figures included herein (discussed in Example 4 below). Such determinations can be performed in a combined or matrix-based manner, such as a combined immunoassay.

In another embodiment, the method includes determining the expression levels of any biomarkers from at least two tables (e.g., Tables 2 and 3). In another embodiment, the method includes determining the expression levels of biomarkers 1-20 (or, when less than 20, the most listed biomarkers) from a specific table and biomarkers 1-20 (or, when less than 20, the most listed biomarkers) from different tables, preferably biomarkers 1-10 from one or two tables, more preferably biomarkers 1-8 from one or two tables, even more preferably biomarkers 1-6 from one or two tables, most preferably biomarkers 1-4 from one or two tables, or subgroups of biomarkers from any of these groups. In another embodiment, the method includes determining the expression levels of any combination of multiple biomarkers from one specific table and one different table. In another embodiment, the method includes determining the expression levels of any combination of multiple biomarkers from biomarkers 1-20 of a specific table (or, when less than 20, the most listed biomarkers) and any combination of multiple biomarkers from biomarkers 1-20 of a different table (or, when less than 20, the most listed biomarkers), preferably any combination of multiple biomarkers from biomarkers 1-10 of one or two tables, more preferably any combination of multiple biomarkers from biomarkers 1-8 of one or two tables, even more preferably any combination of multiple biomarkers from biomarkers 1-6 of one or two tables, most preferably any combination of multiple biomarkers from biomarkers 1-4 of one or two tables, or a subgroup of biomarkers from any of these groups. In another embodiment, the multiple biomarkers from one table are not present in any other table. In a preferred embodiment, the method includes determining the expression levels of any specific subgroup of three biomarkers selected from biomarkers 1-6, 1-8, 1-10, 1-15, or 1-20 of a specific table, and any specific subgroup of three biomarkers selected from biomarkers 1-6, 1-8, 1-10, 1-15, or 1-20 of another table. Alternatively, the method includes determining the expression levels of any specific subgroup of four, five, six, or seven biomarkers selected from biomarkers 1-8, 1-10, 1-15, or 1-20 of a specific table, and any specific subgroup of four, five, six, or seven biomarkers selected from biomarkers 1-8, 1-10, 1-15, or 1-20 of another table. Alternatively, the method includes determining the expression levels of any specific subgroup of biomarkers 8, 9, 10, 11, 12, or 13 selected from biomarkers 1-15 or 1-20 of a specific table, and any specific subgroup of biomarkers 8, 9, 10, 11, 12, or 13 selected from biomarkers 1-15 or 1-20 of another table. Of course, those skilled in the art will recognize that simultaneously determining the expression levels of other biomarkers, whether related to or unrelated to the target disease, is within the scope of this invention.

It will be understood that the same combination type can be applied when the method includes determining the expression level of any biomarker from at least three different tables (e.g., Tables 2, 3, and 4). For example, in one embodiment, the method includes determining the expression level of any combination of multiple biomarkers from biomarkers 1-20 of the first table (or, when less than 20, the most listed biomarkers), any combination of multiple biomarkers from biomarkers 1-20 of the second table (or, when less than 20, the most listed biomarkers), and any combination of multiple biomarkers from biomarkers 1-20 of the third table (or, when less than 20, the most listed biomarkers), preferably any combination of multiple biomarkers from biomarkers 1-10 of each table, more preferably any combination of multiple biomarkers from biomarkers 1-8 of each table, even more preferably any combination of multiple biomarkers from biomarkers 1-6 of each table, and most preferably any combination of multiple biomarkers from biomarkers 1-4 of each table. In a preferred mode, the method includes determining the expression levels of any specific subgroup of three biomarkers selected from biomarkers 1-6, 1-8, 1-10, 1-15, or 1-20 of the first table; any specific subgroup of three biomarkers selected from biomarkers 1-6, 1-8, 1-10, 1-15, or 1-20 of the second table; and any specific subgroup of three biomarkers selected from biomarkers 1-6, 1-8, 1-10, 1-15, or 1-20 of the third table. Alternatively, the method includes determining the expression levels of any specific subgroup of 4, 5, 6, or 7 biomarkers selected from biomarkers 1-8, 1-10, 1-15, or 1-20 of the first table; any specific subgroup of 4, 5, 6, or 7 biomarkers selected from biomarkers 1-8, 1-10, 1-15, or 1-20 of the second table; and any specific subgroup of 4, 5, 6, or 7 biomarkers selected from biomarkers 1-8, 1-10, 1-15, or 1-20 of the third table. Alternatively, the method includes determining the expression levels of any specific subgroup of biomarkers 8, 9, 10, 11, 12, or 13 selected from biomarkers 1-15 or 1-20 of the first table, any specific subgroup of biomarkers 8, 9, 10, 11, 12, or 13 selected from biomarkers 1-15 or 1-20 of the second table, and any specific subgroup of biomarkers 8, 9, 10, 11, 12, or 13 selected from biomarkers 1-15 or 1-20 of the third table. Of course, those skilled in the art will recognize that simultaneously determining the expression levels of other biomarkers, whether related to or unrelated to the target disease, is within the scope of this invention.

The determination of expression levels of multiple biomarkers enables the observation of patterns of expression changes, which provide more sensitive and accurate diagnoses than detecting individual biomarkers. Such determinations can be performed in composite or matrix-based forms, such as composite immunoassays.

In other implementations, the expression level of no more than 5, 10, 15, 20, 25, 30, 35, or 40 markers was determined.

The use of known relationships between specific biomarkers and their first-order interactors facilitates the selection of biomarkers for diagnostic or predictive assays. Many (if not all) biomarkers identified by the inventors (see Tables 1-23) are involved in multiple communication pathways in cells or organs. A deviation from normality in one component of a predicted communication pathway is accompanied by a corresponding deviation in other members of that pathway. Those skilled in the art can readily link members of communication pathways using various databases and available bioinformatics software (see, for example, Ariadne PATHWAY STUDIO, Ariadne, Inc., <www.ariadne.genomics.com> or ChEMBL Database, European Bioinformatics Institute, European Molecular Biology Laboratory, <www.ebi.ac.uk>). Diagnostic methods are likely to maximize the information gathered by measuring biomarker levels based on determining whether multiple biomarkers are abnormal, including some that are not in the same communication pathway as other biomarkers among the multiple biomarkers.

It is also important to understand that the various combinations of biomarkers discussed above can be applied to the design of reagent kits and the reagent kits described in this article.

It is important to understand that the selection criteria discussed above, including preferences for specific subgroups of markers, can be referenced in tables relevant to the specific methods and adopted in any method described herein.

physiological qualitative

This invention relates to methods for the qualitative physiological characterization of individuals in various populations, as described below. As used herein, the qualitative physiological characterization methods according to the invention include methods for diagnosing specific diseases, methods for predicting the likelihood of an individual's response to a therapeutic intervention, methods for monitoring an individual's response to a therapeutic intervention, methods for determining whether an individual has an individual disease risk, methods for determining the degree of risk for a specific disease, methods for grading the severity of a patient's disease, and methods for distinguishing diseases with some of the same symptoms. Typically, these methods rely on determining the expression levels of specific biomarkers as described above.

A. General Group

This invention provides a method for qualitative physiological assessment in subjects. In one embodiment, the invention provides a method for qualitative physiological assessment in subjects, comprising determining the expression level of at least one biomarker from Table 1A in a physiological sample of the subject, wherein the expression level of the at least one biomarker indicates a lung disease such as reactive airway disease or non-small cell lung cancer, or helps differentiate between reactive airway disease and non-small cell lung cancer. In another embodiment, the method comprises determining the expression level of at least one biomarker from Table 1B, wherein the expression level of the at least one biomarker indicates a reactive airway disease or non-small cell lung cancer, or helps differentiate between reactive airway disease and non-small cell lung cancer. In yet another embodiment, the method comprises determining the expression level of at least one biomarker from Table 1C, wherein the expression level of the at least one biomarker indicates a reactive airway disease or non-small cell lung cancer.

In another embodiment, the method includes determining the expression level of SEQ ID NO: 12. In another embodiment, the method includes determining the expression level of SEQ ID NO: 12 and any one of SEQ ID NO: 1-11 and 13-17.

In a preferred embodiment, the present invention provides a method for physiological qualitative analysis in a subject, comprising determining the expression levels of multiple biomarkers from Table 1A in a physiological sample of the subject, wherein the expression patterns of said multiple biomarkers are associated with changes in physiological state or condition, or disease state (e.g., stage of non-small cell lung cancer) or condition. In another preferred embodiment, the expression patterns of the multiple biomarkers from Table 1A indicate lung diseases such as non-small cell lung cancer or reactive airway disease, or help distinguish between reactive airway disease and non-small cell lung cancer. Preferably, the multiple biomarkers are selected based on a low probability of misclassification (based on the Student's t-value calculated as in the examples). In another preferred embodiment, the expression patterns of the biomarkers from Table 1A are associated with an increased likelihood that the subject has or may have a specific disease or condition. In a more preferred embodiment, the method of determining the expression levels of the multiple biomarkers from Table 1A detects an increased likelihood that the subject is developing, has, or may have a lung disease such as non-small cell lung cancer or reactive airway disease (e.g., asthma). The expression patterns can be qualitatively determined using any technique known in the field of pattern recognition technology. The aforementioned biomarkers may include any combination of the biomarkers described above with reference to Table 1A.

The present invention also provides a method for physiological qualitative analysis in subjects, comprising determining the expression level of SEQ ID NO: 12 in a physiological sample of the subject, wherein the expression level of SEQ ID NO: 12 indicates lung disease such as reactive airway disease or non-small cell lung cancer. In a preferred embodiment, the expression patterns of SEQ ID NO: 12 and multiple markers of any of SEQ ID NO: 1-11 and 13-17 are determined and used as described herein.

In another aspect, the present invention provides a method for physiological qualitative analysis in a subject, comprising (a) obtaining a physiological sample of the subject; (b) determining the expression level of at least one polypeptide selected from SEQ ID NO: 1-17 in the subject; and (c) determining the expression level of at least one biomarker from Table 1A in the subject, wherein the expression levels of both the polypeptide and the biomarker from Table 1A indicate lung disease such as non-small cell lung cancer or reactive airway disease. In another embodiment, the expression patterns of multiple markers of SEQ ID NO: 1-17 and multiple biomarkers from Table 1A are determined and used as described herein.

In one implementation, the subject is at risk for non-small cell lung cancer or a reactive airway disease (e.g., asthma, chronic obstructive pulmonary disease, etc.). "At-risk" subjects include individuals who are asymptomatic but are more likely to develop the disease than most due to personal or family history, behavior, exposure to disease-inducing agents (e.g., carcinogens), or some other reason. "At-risk" individuals are routinely identified by pooling risk factors determined for said individuals. This invention provides enhanced detection of "at-risk" individuals by determining the expression levels of relevant biomarkers. In one implementation, the level of a specific disease-related biomarker (specifically, biomarkers from Table 2 or Table 3) is determined for the individual, and a level that differs from all levels expected in the normal population indicates that the individual is "at-risk." In another implementation, the number of relevant biomarkers (from Table 2 or Table 3, applicable to the disease) that are statistically biased from normal is determined, with a larger number of biased markers indicating a greater risk.

The embodiments described above refer to the biomarkers in Table 1A. However, it should be clear that the biomarkers in Tables 1B or 1C can replace the biomarkers in Table 1A in any of the embodiments described. It should also be clear that the various biomarkers to be measured in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Measurement”.

B. Male Group

This invention provides a method for performing physiological qualitative analysis in male subjects. In one embodiment, the method includes obtaining a sample from the male subject and determining the expression level of at least one biomarker from Table 5A or 16A in the physiological sample of the male subject, wherein the expression level of the at least one biomarker indicates a lung disease such as reactive airway disease or non-small cell lung cancer, or helps differentiate between reactive airway disease and non-small cell lung cancer. In another embodiment, the method includes determining the expression level of at least one biomarker from Table 5B or 16B, wherein the expression level of the at least one biomarker indicates a reactive airway disease or non-small cell lung cancer, or helps differentiate between reactive airway disease and non-small cell lung cancer. In yet another embodiment, the method includes determining the expression level of at least one biomarker from Table 5C or 16C, wherein the expression level of the at least one biomarker indicates a reactive airway disease or non-small cell lung cancer.

In a preferred embodiment, the present invention provides a method for physiological qualitative analysis in male subjects, comprising determining the expression levels of multiple biomarkers from Table 5A or 16A in physiological samples of the male subjects, wherein the expression patterns of said multiple biomarkers are associated with changes in physiological state or condition, or disease state (e.g., stage of non-small cell lung cancer) or condition. In another preferred embodiment, the expression patterns of the multiple biomarkers from Table 5A or 16A indicate lung diseases such as non-small cell lung cancer or reactive airway disease, or help distinguish between reactive airway disease and non-small cell lung cancer. Preferably, the multiple biomarkers are selected based on a low probability of misclassification (based on the Student's t-value calculated as in the examples). In another preferred embodiment, the expression patterns of the biomarkers from Table 5A or 16A are associated with an increased likelihood that the male subject has or may have a specific disease or condition. In a more preferred embodiment, the method for determining the expression levels of multiple biomarkers from Table 5A or 16A detects an increased likelihood that the male subject is developing, has, or may have a lung disease such as non-small cell lung cancer or reactive airway disease (e.g., asthma). Expression patterns can be characterized using any technique known in the field of pattern recognition. The various biomarkers may comprise any combination of the biomarkers described above with reference to Tables 5A or 16A.

In another aspect, the present invention provides a method for physiological qualitative analysis in male subjects, comprising (a) obtaining a physiological sample from the male subject; (b) determining the expression level of at least one polypeptide selected from SEQ ID NO: 1-17 in the subject; and (c) determining the expression level of at least one biomarker from Table 5A or 16A in the subject, wherein the expression levels of both the polypeptide and the biomarker from Table 5A or 16A indicate lung disease such as non-small cell lung cancer or reactive airway disease. In another embodiment, the expression levels of multiple markers from SEQ ID NO: 1-17 and multiple biomarkers from Table 5A or 16A are determined and used as described herein.

In one implementation, male subjects are at risk for non-small cell lung cancer or lung disease with reactive airway disorders (e.g., asthma, chronic obstructive pulmonary disease, etc.). "At-risk" subjects and individuals have been discussed above. In one implementation, the levels of specific disease-related biomarkers (specifically, biomarkers from Tables 6, 7, 17, or 18) are determined for male individuals, and those levels that differ from all levels in the expected normal population indicate that the male individual is "at-risk." In another implementation, the number of relevant biomarkers (from Tables 6, 7, 17, or 18, applicable to the diseases) that are statistically biased against normality is determined, with a larger number of biased biomarkers indicating a greater risk.

The embodiments described above refer to the biomarkers in Tables 5A or 16A. However, it should be clear that in any of the embodiments described, biomarkers in Tables 5B or 5C may replace biomarkers in Table 5A, and biomarkers in Tables 16B or 16C may replace biomarkers in Table 16A. It should also be clear that the various biomarkers to be measured in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Assay.”

C. Women's Group

This invention provides a method for performing physiological qualitative analysis in female subjects. In one embodiment, the method includes obtaining a sample from the female subject and determining the expression level of at least one biomarker from Table 9A or 20A in the physiological sample of the female subject, wherein the expression level of the at least one biomarker indicates a lung disease such as reactive airway disease or non-small cell lung cancer, or helps differentiate between reactive airway disease and non-small cell lung cancer. In another embodiment, the method includes determining the expression level of at least one biomarker from Table 9B or 20B, wherein the expression level of the at least one biomarker indicates a reactive airway disease or non-small cell lung cancer, or helps differentiate between reactive airway disease and non-small cell lung cancer. In yet another embodiment, the method includes determining the expression level of at least one biomarker from Table 9C or 20C, wherein the expression level of the at least one biomarker indicates a reactive airway disease or non-small cell lung cancer.

In a preferred embodiment, the present invention provides a method for physiological qualitative analysis in female subjects, comprising determining the expression levels of multiple biomarkers from Table 9A or 20A in physiological samples of the female subjects, wherein the expression patterns of said multiple biomarkers are associated with changes in physiological state or condition, or disease state (e.g., stage of non-small cell lung cancer) or condition. In another preferred embodiment, the expression patterns of the multiple biomarkers from Table 9A or 20A indicate lung diseases such as non-small cell lung cancer or reactive airway disease, or help distinguish between reactive airway disease and non-small cell lung cancer. Preferably, the multiple biomarkers are selected based on a low probability of misclassification (based on the Student's t-value calculated as in the examples). In another preferred embodiment, the expression patterns of the multiple biomarkers from Table 9A or 20A are associated with an increased likelihood that the female subject has or may have a specific disease or condition. In a more preferred embodiment, the method for determining the expression levels of the multiple biomarkers from Table 9A or 20A detects an increased likelihood that the female subject is developing, has, or may have a lung disease such as non-small cell lung cancer or reactive airway disease (e.g., asthma). Expression patterns can be characterized using any technique known in the field of pattern recognition. The various biomarkers may comprise any combination of the biomarkers described above with reference to Tables 9A or 20A.

In another aspect, the present invention provides a method for physiological qualitative analysis in female subjects, comprising (a) obtaining a physiological sample from the female subject; (b) determining the expression level of at least one polypeptide selected from SEQ ID NO: 1-17 in the subject; and (c) determining the expression level of at least one biomarker from Table 9A or 20A in the subject, wherein the expression levels of both the polypeptide and the biomarker from Table 9A or 20A indicate lung disease such as non-small cell lung cancer or reactive airway disease. In another embodiment, the expression patterns of multiple markers from SEQ ID NO: 1-17 and multiple biomarkers from Table 9A or 20A are determined and used as described herein.

In one implementation, the female subject is at risk for non-small cell lung cancer or a lung disease with reactive airway disorders (e.g., asthma, chronic obstructive pulmonary disease, etc.). "At-risk" subjects and individuals have been discussed above. In one implementation, the level of a specific disease-related biomarker (specifically, biomarkers from Tables 10, 11, 21, or 22) is determined for each female individual, and the level that differs from all levels in the expected normal population indicates that the female individual is "at-risk." In another implementation, the number of relevant biomarkers (from Tables 10, 11, 21, or 22, applicable to the disease) that are statistically biased against normality is determined, with a larger number of biased biomarkers indicating a greater risk.

The embodiments described above refer to the biomarkers in Tables 9A or 20A. However, it should be clear that in any of the embodiments described, biomarkers in Tables 9B or 9C may replace biomarkers in Table 9A, and biomarkers in Tables 20B or 20C may replace biomarkers in Table 9A. It should also be clear that the various biomarkers to be measured in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Assay.”

Lung diseases

This invention provides various diagnostic and predictive methods for lung diseases. Specifically, this invention provides methods for diagnosing reactive airway diseases, particularly those associated with overreactive TH2 and TH17 cells. Reactive airway diseases include asthma, chronic obstructive pulmonary disease (COPD), allergic rhinitis, cystic fibrosis, bronchitis, or other diseases exhibiting hyperresponsiveness to a variety of physiological and/or environmental stimuli. More specifically, this invention provides methods for diagnosing asthma and COPD, and more particularly, methods for diagnosing asthma.

The present invention also provides methods for diagnosing non-small cell lung cancer. These methods include determining the expression level of at least one biomarker described herein, wherein the biomarker indicates the presence or development of non-small cell lung cancer. For example, the expression level of the biomarker described herein can be used to determine the progression of non-small cell lung cancer, the presence of precancerous lesions, or the stage of non-small cell lung cancer.

In this specific implementation, subjects were selected from individuals exhibiting one or more symptoms of non-small cell lung cancer or reactive airway disease. Symptoms may include cough, shortness of breath, wheezing, chest pain, and hemoptysis; shoulder pain descending from the outer side of the arm or hoarseness due to vocal cord paralysis; and esophageal invasion, which may cause dysphagia. If large airways are obstructed, partial collapse of the lungs may occur, leading to infection and abscesses or pneumonia. Metastasis to the bone can cause severe pain. Metastasis to the brain can cause neurological symptoms including dizziness, headache, cramps, or symptoms commonly associated with stroke, such as weakness or loss of sensation in parts of the body. Lung cancer often produces symptoms caused by hormone-like substances produced by tumor cells. A common paraneoplastic syndrome in NSCLC is the production of parathyroid hormone-like substances, which causes elevated calcium levels in the bloodstream. Asthma generally produces symptoms such as cough (especially at night), wheezing, shortness of breath, and a feeling of tightness in the chest, chest pain, or chest pressure. Therefore, many asthma symptoms are clearly shared with NSCLC.

Methods for diagnosing reactive airway diseases

This invention relates to methods for diagnosing reactive airway disease in individuals from various populations described below. Typically, these methods rely on determining the expression levels of specific biomarkers as described herein.

A. General Group

The present invention provides a method for diagnosing reactive airway disease in a subject, comprising (a) obtaining a physiological sample of the subject; and (b) determining the expression level of at least one biomarker from Table 2 in the subject, wherein the expression level of the at least one biomarker indicates reactive airway disease.

In a preferred embodiment, the present invention provides a method for diagnosing reactive airway disease in a subject, comprising determining the expression levels of multiple biomarkers from Table 2 in a physiological sample of the subject, wherein the expression patterns of said multiple biomarkers indicate reactive airway disease or are associated with changes in reactive airway disease status. In another preferred embodiment, the expression patterns are associated with an increased likelihood that the subject has or may have reactive airway disease. The expression patterns can be characterized by any technique known in the field of pattern recognition. The multiple biomarkers may comprise any combination of the biomarkers described above with reference to Table 2. In fact, it is to be clear that the multiple biomarkers to be measured in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Measurement”.

In one implementation, the subject is at risk of reactive airway disease. In one implementation, the levels of specific biomarkers associated with reactive airway disease are determined for each individual, and levels that differ from those expected in the normal population indicate that the individual is "at risk." In another implementation, the number of relevant biomarkers from Table 2 that deviate statistically from normal values is determined, with a larger number of deviating biomarkers indicating a greater risk of reactive airway disease. In yet another implementation, the subject is selected from individuals exhibiting one or more symptoms of reactive airway disease.

In any of the embodiments described above, the preferred biomarkers used in this method include at least one biomarker from Table 13B. More preferably, all biomarkers in this embodiment can be found in Table 13B.

B. Male Group

The present invention provides a method for diagnosing reactive airway disease in male subjects, comprising (a) obtaining a physiological sample of the male subject; and (b) determining the expression level of at least one biomarker from Table 6 or 17 in the subject, wherein the expression level of the at least one biomarker indicates reactive airway disease.

In a preferred embodiment, the present invention provides a method for diagnosing reactive airway disease in male subjects, comprising determining the expression levels of multiple biomarkers from Table 6 or 17 in a physiological sample of the male subject, wherein the expression patterns of said multiple biomarkers indicate reactive airway disease or are associated with changes in reactive airway disease status. In another preferred embodiment, the expression patterns are associated with an increased likelihood that the male subject has or may have reactive airway disease. The expression patterns can be characterized by any technique known in the field of pattern recognition. The multiple biomarkers may comprise any combination of the biomarkers described above with reference to Table 6 or 17. In fact, it is to be clear that the multiple biomarkers to be measured in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Measurement”.

In one implementation, male subjects are at risk of reactive airway disease. In one implementation, the levels of specific biomarkers associated with reactive airway disease in male individuals are determined, and levels that differ from those expected in a normal male population indicate that the individual is "at risk." In another implementation, the number of relevant biomarkers from Table 6 that deviate statistically from normal values is determined, with a larger number of deviating biomarkers indicating a greater risk of reactive airway disease. In yet another implementation, male subjects are selected from individuals exhibiting one or more symptoms of reactive airway disease.

In another embodiment, the biomarkers used in this method include at least one biomarker from Table 13A.

C. Women's Group

The present invention provides a method for diagnosing reactive airway disease in female subjects, comprising (a) obtaining a physiological sample of the female subject; and (b) determining the expression level of at least one biomarker from Table 10 or 21 in the subject, wherein the expression level of the at least one biomarker indicates reactive airway disease.

In a preferred embodiment, the present invention provides a method for diagnosing reactive airway disease in female subjects, comprising determining the expression levels of multiple biomarkers from Table 10 or 21 in a physiological sample of the female subject, wherein the expression patterns of said multiple biomarkers indicate reactive airway disease or are associated with changes in reactive airway disease status. In another preferred embodiment, the expression patterns are associated with an increased likelihood that the female subject has or may have reactive airway disease. The expression patterns can be characterized by any technique known in the field of pattern recognition. The multiple biomarkers may comprise any combination of the biomarkers described above with reference to Table 10 or 21. In fact, it is to be clear that the multiple biomarkers to be measured in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Measurement”.

In one implementation, female subjects are at risk of reactive airway disease. In one implementation, the levels of specific biomarkers associated with reactive airway disease in individual women are determined, and levels that differ from those expected in a normal female population indicate that the individual is "at risk." In another implementation, the number of relevant biomarkers from Table 10 or 21 that deviate statistically from normal are determined, with a larger number of deviating biomarkers indicating a greater risk of reactive airway disease. In yet another implementation, female subjects are selected from individuals exhibiting one or more symptoms of reactive airway disease.

In another embodiment, the biomarkers used in this method include at least one biomarker from Table 13A.

Methods for diagnosing non-small cell lung cancer

This invention relates to methods for diagnosing non-small cell lung cancer in individuals from various populations described below. Typically, these methods rely on measuring the expression levels of specific biomarkers as described herein.

A. General Group

The present invention provides a method for diagnosing non-small cell lung cancer in a subject, comprising (a) obtaining a physiological sample of the subject; and (b) determining the expression level of at least one biomarker from Table 3 in the subject, wherein the expression level of the at least one biomarker indicates the presence or development of non-small cell lung cancer.

In one preferred embodiment, the present invention provides a method for diagnosing non-small cell lung cancer in a subject, comprising measuring the expression levels of multiple biomarkers from Table 3 in a physiological sample of the subject, wherein the expression patterns of said multiple biomarkers indicate non-small cell lung cancer or are associated with changes in the disease state (i.e., clinical or diagnostic stage) of non-small cell lung cancer. In another preferred embodiment, the expression patterns are associated with an increased likelihood that the subject has or may have non-small cell lung cancer. The expression patterns can be characterized by any technique known in the field of pattern recognition. The multiple biomarkers may comprise any combination of the biomarkers described above with reference to Table 3. In fact, it is to be clear that the multiple biomarkers to be measured in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Measurement”.

In one implementation, the subject is at risk of developing non-small cell lung cancer (NSCLC). In one implementation, the levels of specific biomarkers associated with NSCLC are determined for each individual, and levels that differ from those expected in the normal population indicate that the individual is "at risk." In another implementation, the number of relevant biomarkers from Table 3 that deviate statistically from normal values is determined, with a larger number of deviating biomarkers indicating a greater risk of NSCLC. In yet another implementation, the subject is selected from individuals exhibiting one or more symptoms of NSCLC.

In any of the embodiments described above, the preferred biomarkers used in this method include at least one biomarker from Table 14B. More preferably, all biomarkers in this embodiment can be found in Table 14B.

B. Male Group

The present invention also provides a method for diagnosing non-small cell lung cancer in male subjects, comprising (a) obtaining a physiological sample of the male subject; and (b) determining the expression level of at least one biomarker from Table 7 or 18 in the subject, wherein the expression level of the at least one biomarker indicates the presence and development of non-small cell lung cancer.

In a preferred embodiment, the present invention provides a method for diagnosing non-small cell lung cancer (NSCLC) in male subjects, comprising determining the expression levels of multiple biomarkers from Tables 7 or 18 in a physiological sample of the male subject, wherein the expression patterns of said multiple biomarkers indicate NSCLC or are associated with changes in NSCLC disease status (e.g., stages). In another preferred embodiment, the expression patterns are associated with an increased likelihood that the male subject has or is likely to have NSCLC. The expression patterns can be characterized using any technique known in the field of pattern recognition. The multiple biomarkers may comprise any combination of the biomarkers described above with reference to Tables 7 or 18. In fact, it is to be clear that the multiple biomarkers to be measured in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Measurement”.

In one implementation, the male subjects are at risk of developing non-small cell lung cancer (NSCLC). In one implementation, the levels of specific biomarkers associated with NSCLC in male individuals are determined, and levels that differ from those expected in the normal male population indicate that the individual is "at risk." In another implementation, the number of relevant biomarkers from Table 7 that deviate statistically from normal values is determined, with a larger number of deviating biomarkers indicating a greater risk of NSCLC. In yet another implementation, the male subjects are selected from individuals exhibiting one or more symptoms of NSCLC.

In another embodiment, the biomarkers used in this method include at least one biomarker from Table 14A.

C. Women's Group

The present invention also provides a method for diagnosing non-small cell lung cancer in female subjects, comprising (a) obtaining a physiological sample of the female subject; and (b) determining the expression level of at least one biomarker from Table 11 or 22 in the subject, wherein the expression level of the at least one biomarker indicates the presence or development of non-small cell lung cancer.

In a preferred embodiment, the present invention provides a method for diagnosing non-small cell lung cancer in female subjects, comprising determining the expression levels of multiple biomarkers from Table 11 or 22 in a physiological sample of the female subject, wherein the expression patterns of said multiple biomarkers indicate non-small cell lung cancer or are associated with changes in non-small cell lung cancer disease status (e.g., various stages). In another preferred embodiment, the expression patterns are associated with an increased likelihood that the female subject has or is likely to have non-small cell lung cancer. The expression patterns can be characterized by any technique known in the field of pattern recognition. The multiple biomarkers may comprise any combination of the biomarkers described above with reference to Table 11 or 22. In fact, it is to be clear that the multiple biomarkers to be measured in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Measurement”.

In one implementation, the female subject is at risk of non-small cell lung cancer (NSCLC). In one implementation, the levels of specific biomarkers associated with NSCLC in the female individual are determined, and levels that differ from those expected in a normal female population indicate that the individual is "at risk." In another implementation, the number of relevant biomarkers from Table 11 or 22 that deviate statistically from normal values are determined, with a larger number of deviating biomarkers indicating a greater risk of NSCLC. In yet another implementation, the female subject is selected from individuals exhibiting one or more symptoms of NSCLC.

In another embodiment, the biomarkers used in this method include at least one biomarker from Table 14A.

Methods to differentiate between non-small cell lung cancer and reactive airway disease

This invention relates to methods for diagnosing lung diseases in individuals from various populations described below. Typically, these methods rely on determining the expression levels of specific biomarkers that differentiate reactive airway disease from non-small cell lung cancer.

A. General Group

The present invention also provides a method for diagnosing lung disease in a subject, comprising determining the expression level of at least one biomarker from Table 4 in the subject, wherein the expression level of the at least one biomarker from Table 4 assists in distinguishing between reactive airway disease and non-small cell lung cancer. In one embodiment, the subject has been diagnosed with reactive airway disease and/or non-small cell lung cancer. For example, the diagnosis has been determined by the expression level of at least one biomarker in a physiological sample of the subject, wherein the expression level of the at least one biomarker indicates reactive airway disease and/or non-small cell lung cancer.

The present invention also provides a method for diagnosing lung disease in a subject, comprising (a) obtaining a physiological sample of the subject; and (b) determining the expression levels of at least one biomarker from Table 4, at least one biomarker from Table 2, and at least one biomarker from Table 3 in the subject, wherein (i) the at least one biomarker from each of Tables 2, 3, and 4 is not the same, (ii) the expression levels of the at least one biomarker from Tables 2 and 3 respectively indicate reactive airway disease and non-small cell lung cancer; and (iii) the expression level of the at least one biomarker from Table 4 assists in distinguishing between non-small cell lung cancer and reactive airway disease. Preferably, this method includes at least one biomarker from each of the tables but not present in any of the other tables.

In a preferred embodiment, the method includes determining the expression levels of multiple biomarkers from Table 4, preferably also multiple biomarkers from Table 2, and multiple biomarkers from Table 3. In another preferred embodiment, the expression pattern is associated with an increased likelihood that the subject has non-small cell lung cancer or reactive airway disease. The expression pattern can be characterized using any technique known in the field of pattern recognition technology. The multiple biomarkers may comprise any combination of the biomarkers described above with reference to Tables 2, 3, and 4. In fact, it is clear that the multiple biomarkers to be determined in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Assay”.

In one implementation, the subject is at risk of developing non-small cell lung cancer and/or reactive airway disease. In another implementation, the subject is selected from individuals exhibiting one or more symptoms of non-small cell lung cancer and/or reactive airway disease.

The present invention also provides diagnostic methods to help distinguish the likelihood of a subject having the risk of developing or having non-small cell lung cancer or reactive airway disease, including (a) obtaining a physiological sample of a subject at risk of having non-small cell lung cancer or reactive airway disease; and (b) determining the expression level of at least one biomarker from Table 4 in the subject, wherein the expression level of the at least one biomarker from Table 4 helps to distinguish the likelihood of the subject having non-small cell lung cancer or reactive airway disease.

In one preferred embodiment, the method includes determining the expression levels of a plurality of biomarkers from Table 4. In another preferred embodiment, the expression pattern is associated with an increased likelihood that the subject has non-small cell lung cancer or reactive airway disease. The expression pattern can be characterized by any technique known in the field of pattern recognition technology. The plurality of biomarkers may comprise any combination of the biomarkers described above with reference to Table 4. In fact, it is to be clear that the plurality of biomarkers to be determined in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Assay”.

In one implementation, the subjects are selected from individuals exhibiting one or more symptoms of non-small cell lung cancer or reactive airway disease. Methods relating to “at-risk” subjects are described above, and related methods are included within the scope of this invention.

B. Male Group

The present invention also provides a method for diagnosing lung disease in male subjects, comprising determining the expression level of at least one biomarker from Table 8 or 19 in the subject, wherein the expression level of the at least one biomarker from Table 8 or 19 assists in differentiating between reactive airway disease and non-small cell lung cancer. In one embodiment, the male subject has been diagnosed with reactive airway disease and/or non-small cell lung cancer. For example, the diagnosis has been determined by the expression level of at least one biomarker in a physiological sample of the male subject, wherein the expression level of the at least one biomarker indicates reactive airway disease and/or non-small cell lung cancer.

The present invention also provides a method for diagnosing lung disease in male subjects, comprising (a) obtaining a physiological sample of the male subject; and (b) determining the expression levels of at least one biomarker from Table 8, at least one biomarker from Table 6, and at least one biomarker from Table 7 in the subject, wherein (i) the at least one biomarker from each of Tables 6, 7, and 8 is not the same; (ii) the expression levels of the at least one biomarker from Tables 6 and 7 respectively indicate reactive airway disease and non-small cell lung cancer; and (iii) the expression level of the at least one biomarker from Table 8 assists in distinguishing between non-small cell lung cancer and reactive airway disease. Preferably, this method includes at least one biomarker from each of the tables but not present in any of the other tables.

The present invention also provides a method for diagnosing lung disease in male subjects, comprising (a) obtaining a physiological sample of the male subject; and (b) determining the expression levels of at least one biomarker from Table 19, at least one biomarker from Table 18, and at least one biomarker from Table 17 in the subject, wherein (i) the at least one biomarker from each of Tables 17, 18, and 19 is not the same, (ii) the expression levels of the at least one biomarker from Tables 17 and 18 respectively indicate reactive airway disease and non-small cell lung cancer; and (iii) the expression level of the at least one biomarker from Table 19 assists in distinguishing between non-small cell lung cancer and reactive airway disease. Preferably, this method includes at least one biomarker from each of the tables but not present in any of the other tables.

In a preferred embodiment, the method includes determining the expression levels of multiple biomarkers from Table 8, preferably also multiple biomarkers from Table 6, and multiple biomarkers from Table 7. In another preferred embodiment, the expression pattern is associated with an increased likelihood of male subjects developing non-small cell lung cancer or reactive airway disease. The expression pattern can be characterized using any technique known in the field of pattern recognition. The multiple biomarkers may comprise any combination of the biomarkers described above with reference to Tables 6, 7, and 8. In fact, it is clear that the multiple biomarkers to be determined in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Determination”.

In a preferred embodiment, the method includes determining the expression levels of multiple biomarkers from Table 19, preferably also multiple biomarkers from Table 17, and multiple biomarkers from Table 18. In another preferred embodiment, the expression pattern is associated with an increased likelihood of male subjects developing non-small cell lung cancer or reactive airway disease. The expression pattern can be characterized using any technique known in the field of pattern recognition. The multiple biomarkers may comprise any combination of the biomarkers described above with reference to Tables 17, 18, and 19. In fact, it is clear that the multiple biomarkers to be determined in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Determination”.

In one implementation, the male subjects are at risk of developing non-small cell lung cancer and/or reactive airway disease. In another implementation, the male subjects are selected from individuals exhibiting one or more symptoms of non-small cell lung cancer and/or reactive airway disease.

The present invention also provides diagnostic methods to help distinguish the likelihood of a male subject having the risk of developing or having non-small cell lung cancer or reactive airway disease, comprising (a) obtaining a physiological sample of a male subject at risk of having non-small cell lung cancer or reactive airway disease; and (b) determining the expression level of at least one biomarker from Table 8 or 19 in the subject, wherein the expression level of at least one biomarker from Table 8 or 19 helps to distinguish the likelihood of the subject having non-small cell lung cancer or reactive airway disease.

In one preferred embodiment, the method includes determining the expression levels of a plurality of biomarkers from Table 8. In another preferred embodiment, the expression pattern is associated with an increased likelihood of male subjects developing non-small cell lung cancer or reactive airway disease. The expression pattern can be characterized using any technique known in the field of pattern recognition technology. The plurality of biomarkers may comprise any combination of the biomarkers described above with reference to Tables 8 or 19. In fact, it is to be clear that the plurality of biomarkers to be determined in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Assay”.

In one implementation, male subjects are selected from individuals exhibiting one or more symptoms of non-small cell lung cancer or reactive airway disease. Methods relating to “at-risk” subjects are described above, and related methods are included within the scope of this invention.

C. Women's Group

The present invention also provides a method for diagnosing lung disease in female subjects, comprising determining the expression level of at least one biomarker from Table 12 or 23 in the subject, wherein the expression level of the at least one biomarker from Table 12 or 23 assists in distinguishing between reactive airway disease and non-small cell lung cancer. In one embodiment, the female subject has been diagnosed with reactive airway disease and/or non-small cell lung cancer. For example, the diagnosis has been determined by the expression level of at least one biomarker in a physiological sample of the female subject, wherein the expression level of the at least one biomarker indicates reactive airway disease and/or non-small cell lung cancer.

The present invention also provides a method for diagnosing lung disease in female subjects, comprising (a) obtaining a physiological sample of the female subject; and (b) determining the expression levels of at least one biomarker from Table 12, at least one biomarker from Table 10, and at least one biomarker from Table 11 in the subject, wherein (i) the at least one biomarker from each of Tables 10, 11, and 12 is not the same, (ii) the expression levels of the at least one biomarker from Tables 10 and 11 respectively indicate reactive airway disease and non-small cell lung cancer; and (iii) the expression level of the at least one biomarker from Table 12 assists in distinguishing between non-small cell lung cancer and reactive airway disease. Preferably, this method includes at least one biomarker from each of the tables but not present in any of the other tables.

The present invention also provides a method for diagnosing lung disease in female subjects, comprising (a) obtaining a physiological sample of the female subject; and (b) determining the expression levels of at least one biomarker from Table 23, at least one biomarker from Table 21, and at least one biomarker from Table 22 in the subject, wherein (i) the at least one biomarker from each of Tables 21, 22, and 23 is not the same, (ii) the expression levels of the at least one biomarker from Tables 21 and 22 respectively indicate reactive airway disease and non-small cell lung cancer; and (iii) the expression level of the at least one biomarker from Table 23 assists in distinguishing between non-small cell lung cancer and reactive airway disease. Preferably, this method includes at least one biomarker from each of the tables but not present in any of the other tables.

In a preferred embodiment, the method includes determining the expression levels of multiple biomarkers from Table 12, preferably also multiple biomarkers from Table 10, and multiple biomarkers from Table 11. In another preferred embodiment, the expression pattern is associated with an increased likelihood of male subjects developing non-small cell lung cancer or reactive airway disease. The expression pattern can be characterized using any technique known in the field of pattern recognition. The multiple biomarkers may comprise any combination of the biomarkers described above with reference to Tables 10, 11, and 12. In fact, it is clear that the multiple biomarkers to be determined in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Determination”.

In one preferred embodiment, the method includes determining the expression levels of multiple biomarkers from Table 23, preferably also multiple biomarkers from Table 21, and multiple biomarkers from Table 22. In another preferred embodiment, the expression pattern is associated with an increased likelihood of male subjects developing non-small cell lung cancer or reactive airway disease. The expression pattern can be characterized using any technique known in the field of pattern recognition. The multiple biomarkers may comprise any combination of the biomarkers described above with reference to Tables 21, 22, and 23. In fact, it is clear that the multiple biomarkers to be determined in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Determination”.

In one implementation, the female subjects are at risk of developing non-small cell lung cancer and/or reactive airway disease. In another implementation, the female subjects are selected from individuals exhibiting one or more symptoms of non-small cell lung cancer and/or reactive airway disease.

The present invention also provides diagnostic methods to help distinguish the likelihood of a female subject having the risk of developing or having non-small cell lung cancer or reactive airway disease, comprising (a) obtaining a physiological sample of a female subject at risk of having non-small cell lung cancer or reactive airway disease; and (b) determining the expression level of at least one biomarker from Table 12 or 23 in the subject, wherein the expression level of the at least one biomarker from Table 12 or 23 helps to distinguish the likelihood of the subject having non-small cell lung cancer or reactive airway disease.

In one preferred embodiment, the method includes determining the expression levels of multiple biomarkers from Table 12 or 23. In another preferred embodiment, the expression pattern is associated with an increased likelihood of non-small cell lung cancer or reactive airway disease in female subjects. The expression pattern can be characterized using any technique known in the art of pattern recognition. The multiple biomarkers may comprise any combination of the biomarkers described above with reference to Table 12 or 23. In fact, it is to be clear that the multiple biomarkers to be determined in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Assay”.

In one implementation, female subjects are selected from individuals exhibiting one or more symptoms of non-small cell lung cancer or reactive airway disease. Methods relating to “at-risk” subjects are described above, and related methods are included within the scope of this invention.

In any of the methods described herein that use biomarkers selected from more than one table to differentiate, for example, different disease states or different groups, the analysis of biomarker results from individuals can be performed simultaneously or sequentially.

Methods of monitoring treatment

This invention relates to methods for monitoring treatment in individuals across various populations described below. Typically, these methods rely on measuring the expression levels of specific biomarkers.

A. General Group

The present invention also provides a method for monitoring subjects, comprising (a) determining the first expression level of at least one biomarker from Table 1A in the subject in a sample obtained from the subject; (b) determining the second expression level of the at least one biomarker from Table 1A in the subject using a second sample obtained from the subject at a time different from the first expression level; and (d) comparing the first expression level and the second expression level. Generally, a therapeutic intervention has been performed on the subject between the times of the first and second sample acquisition. Detecting a change in the expression pattern between the first and second measurements can be considered to reflect the effect of the therapeutic intervention. This embodiment can also be used to identify specific biomarkers that exhibit changes in their expression levels in response to a specific therapeutic intervention.

In a preferred embodiment, the method includes determining the expression levels of a plurality of biomarkers from Table 1A. The plurality of biomarkers may include any combination of the biomarkers described above with reference to Table 1A. In fact, it is to be clear that the plurality of biomarkers to be determined in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Determination”.

The embodiments described above refer to the biomarkers in Table 1A. However, it should be understood that in any of the described embodiments, the biomarkers in Table 1B, Table 1C, Table 2, Table 3, or Table 4 may replace the biomarkers in Table 1A.

B. Male Group

The present invention also provides a method for monitoring male subjects, comprising (a) determining the first expression level of at least one biomarker from Table 5A or 16A in the male subject in a sample obtained from the male subject; (b) determining the second expression level of the at least one biomarker from Table 1A or 16A in the male subject using a second sample obtained from the male subject at a time different from the first expression level; and (d) comparing the first expression level and the second expression level. Generally, a therapeutic intervention has been performed on the male subject between the times when the first and second samples were obtained. Detecting a change in the expression pattern between the first and second measurements can be considered to reflect the effect of the therapeutic intervention. This embodiment can also be used to identify specific biomarkers that exhibit changes in their expression levels in response to a specific therapeutic intervention.

In a preferred embodiment, the method includes determining the expression levels of multiple biomarkers from Table 5A or 16A. The multiple biomarkers may comprise any combination of the biomarkers described above in Table 5A or 16A. In fact, it is to be clear that the multiple biomarkers to be determined in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Determination”.

The embodiments described above refer to the biomarkers in Table 5A or 16A. However, it should be understood that in any of the described embodiments, the biomarkers in Table 5B, Table 5C, Table 6, Table 7, Table 8, or Table 16B, Table 16C, Table 17, Table 18, or Table 19 may replace the biomarkers in Table 5A or 16A.

C. Women's Group

The present invention also provides a method for monitoring female subjects, comprising (a) determining the first expression level of at least one biomarker from Table 9A or 20A in the female subject in a sample obtained from the female subject; (b) determining the second expression level of the at least one biomarker from Table 9A or 20A in the female subject using a second sample obtained from the female subject at a time different from the first expression level; and (d) comparing the first expression level and the second expression level. Generally, a therapeutic intervention has been performed on the female subject between the times when the first and second samples were obtained. Detecting a change in the expression pattern between the first and second measurements can be considered to reflect the effect of the therapeutic intervention. This embodiment can also be used to identify specific biomarkers that exhibit changes in their expression levels in response to a specific therapeutic intervention.

In a preferred embodiment, the method includes determining the expression levels of a plurality of biomarkers from Table 9A or 20A. The plurality of biomarkers may comprise any combination of the biomarkers described above with reference to Table 9A or 20A. In fact, it is to be clear that the plurality of biomarkers to be determined in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Determination”.

The embodiments described above refer to the biomarkers in Tables 9A or 20A. However, it should be understood that in any of the described embodiments, the biomarkers in Tables 9B, 9C, 10, 11, 12, or 20B, 20C, 21, 22, or 23 may replace the biomarkers in Table 9A or 20A. Methods for predicting a subject's response to a therapeutic intervention.

This invention relates to methods for predicting a subject's response to a therapeutic intervention in individuals across various populations described below. Typically, these methods rely on measuring the expression levels of specific biomarkers.

A. General Group

The present invention also provides a method for predicting a subject's response to a therapeutic intervention, comprising (a) obtaining a physiological sample of the subject; and (b) determining the expression level of at least one biomarker from Table 1A in the subject, wherein the expression level of the at least one biomarker from Table 1A aids in predicting the subject's response to the therapeutic intervention. Preferred biomarkers for this embodiment are those that, when monitored in a population of subjects, demonstrate a response to the target therapeutic intervention. This embodiment can also be used to select patients who are more likely to respond to treatment.

In a preferred embodiment, the method includes determining the expression levels of a plurality of biomarkers from Table 1A. The plurality of biomarkers may comprise any combination of the biomarkers described above with reference to Table 1A. In fact, it is to be clear that the plurality of biomarkers to be determined in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Determination”.

The embodiments described above refer to the biomarkers in Table 1A. However, it should be understood that in any of the described embodiments, the biomarkers in Table 1B, Table 1C, Table 2, Table 3, or Table 4 may replace the biomarkers in Table 1A.

B. Male Group

The present invention also provides a method for predicting the response of male subjects to a therapeutic intervention, comprising (a) obtaining a physiological sample of the male subject; and (b) determining the expression level of at least one biomarker from Table 5A or 16A in the male subject, wherein the expression level of the at least one biomarker from Table 5A or 16A assists in predicting the male subject's response to the therapeutic intervention. Preferred biomarkers for this embodiment are those that, when monitored in a population of male subjects, show a response to the target therapeutic intervention. This embodiment can also be used to select male patients who are more likely to respond to treatment.

In a preferred embodiment, the method includes determining the expression levels of a plurality of biomarkers from Table 5A or 16A. The plurality of biomarkers may comprise any combination of the biomarkers described above with reference to Table 5A or 16A. In fact, it is to be clear that the plurality of biomarkers to be determined in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Determination”.

The embodiments described above refer to the biomarkers in Table 5A or 16A. However, it should be understood that in any of the described embodiments, the biomarkers in Table 5B, Table 5C, Table 6, Table 7, Table 8, Table 16B, Table 16C, Table 17, Table 18, or Table 19 may replace the biomarkers in Table 5A or 16A.

C. Women's Group

The present invention also provides a method for predicting the response of female subjects to a therapeutic intervention, comprising (a) obtaining a physiological sample of the female subject; and (b) determining the expression level of at least one biomarker from Table 9A or 20A in the female subject, wherein the expression level of the at least one biomarker from Table 9A or 20A assists in predicting the female subject's response to the therapeutic intervention. Preferred biomarkers for this embodiment are those that, when monitored in a population of female subjects, show a response to the target therapeutic intervention. This embodiment can also be used to select female patients who are more likely to respond to treatment.

In a preferred embodiment, the method includes determining the expression levels of a plurality of biomarkers from Table 9A or 20A. The plurality of biomarkers may comprise any combination of the biomarkers described above with reference to Table 9A or 20A. In fact, it is to be clear that the plurality of biomarkers to be determined in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Determination”.

The embodiments described above refer to the biomarkers in Table 9A or 20A. However, it should be understood that in any of the described embodiments, the biomarkers in Table 9B, Table 9C, Table 10, Table 11, Table 12, Table 20B, Table 20C, Table 21, Table 22, or Table 23 may replace the biomarkers in Table 9A or 20A.

Methods for designing reagent kits

A. General Group

The present invention also provides a method for designing a kit for assisting in the diagnosis of lung diseases in subjects, comprising (a) selecting at least one biomarker from Table 1A; (b) selecting a tool for measuring the expression level of said at least one biomarker; and (c) designing a kit comprising the tool for measuring the expression level.

The present invention also provides a method for designing a kit for diagnosing non-small cell lung cancer or reactive airway disease in subjects, comprising (a) selecting at least one biomarker from Table 1B; (b) selecting a tool for measuring the expression level of said at least one biomarker; and (c) designing a kit comprising said tool for measuring the expression level.

The present invention also provides a method for designing a kit for diagnosing non-small cell lung cancer or reactive airway disease in subjects, comprising (a) selecting at least one biomarker from Table 1C; (b) selecting a tool for measuring the expression level of said at least one biomarker; and (c) designing a kit comprising the tool for measuring the expression level.

The present invention also provides a method for designing a kit for diagnosing reactive airway disease in subjects, comprising (a) selecting at least one biomarker from Table 2; (b) selecting a tool for measuring the expression level of said at least one biomarker; and (c) designing a kit comprising said tool for measuring the expression level.

The present invention also provides a method for designing a kit for diagnosing non-small cell lung cancer in subjects, comprising (a) selecting at least one biomarker from Table 3; (b) selecting a tool for measuring the expression level of said at least one biomarker; and (c) designing a kit comprising said tool for measuring the expression level.

The present invention also provides a method for designing a kit for assisting in the diagnosis of lung diseases in subjects, comprising (a) selecting at least one biomarker from Table 4; (b) selecting a tool for measuring the expression level of said at least one biomarker; and (c) designing a kit comprising said tool for measuring the expression level.

In the method described above, steps (b) and (c) may alternatively be performed by: (b) selecting a detection reagent for detecting the at least one biomarker and (c) designing a kit comprising the detection reagent for detecting the at least one biomarker.

The present invention also provides a method for designing a kit, including selecting at least one biomarker from more than one table. For example, the present invention provides a method for designing a kit, including selecting at least one biomarker from Table 2 and at least one biomarker from Table 3. In another example, the present invention provides a method for designing a kit, including selecting at least one biomarker from Table 2, at least one biomarker from Table 3, and at least one biomarker from Table 4. It is to be understood that these methods also include steps (b) and (c) as described above.

It is important to understand that the various biomarkers to be measured in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Measurement”.

B. Male Group

The present invention also provides a method for designing a kit for assisting in the diagnosis of lung disease in male subjects, comprising (a) selecting at least one biomarker from Table 5A or 16A; (b) selecting a tool for measuring the expression level of said at least one biomarker; and (c) designing a kit comprising said tool for measuring the expression level.

The present invention also provides a method for designing a kit for diagnosing non-small cell lung cancer or reactive airway disease in male subjects, comprising (a) selecting at least one biomarker from Table 5B or 16B; (b) selecting a tool for measuring the expression level of said at least one biomarker; and (c) designing a kit comprising said tool for measuring the expression level.

The present invention also provides a method for designing a kit for diagnosing non-small cell lung cancer or reactive airway disease in male subjects, comprising (a) selecting at least one biomarker from Table 5C or 16C; (b) selecting a tool for measuring the expression level of said at least one biomarker; and (c) designing a kit comprising the tool for measuring the expression level.

The present invention also provides a method for designing a kit for diagnosing reactive airway disease in male subjects, comprising (a) selecting at least one biomarker from Table 6 or 17; (b) selecting a tool for measuring the expression level of said at least one biomarker; and (c) designing a kit comprising said tool for measuring the expression level.

The present invention also provides a method for designing a kit for diagnosing non-small cell lung cancer in male subjects, comprising (a) selecting at least one biomarker from Table 7 or 18; (b) selecting a tool for measuring the expression level of said at least one biomarker; and (c) designing a kit comprising said tool for measuring the expression level.

The present invention also provides a method for designing a kit for assisting in the diagnosis of lung disease in male subjects, comprising (a) selecting at least one biomarker from Table 8 or 19; (b) selecting a tool for measuring the expression level of said at least one biomarker; and (c) designing a kit comprising said tool for measuring the expression level.

In the method described above, steps (b) and (c) may alternatively be performed by: (b) selecting a detection reagent for detecting the at least one biomarker and (c) designing a kit comprising the detection reagent for detecting the at least one biomarker.

The present invention also provides a method for designing a reagent kit, comprising selecting at least one biomarker from more than one table. For example, the present invention provides a method for designing a reagent kit comprising selecting at least one biomarker from Table 6 and at least one biomarker from Table 7. In another example, the present invention provides a method for designing a reagent kit comprising selecting at least one biomarker from Table 6, at least one biomarker from Table 7, and at least one biomarker from Table 8. In another example, the present invention provides a method for designing a reagent kit comprising selecting at least one biomarker from Table 17 and at least one biomarker from Table 18. In yet another example, the present invention provides a method for designing a reagent kit comprising selecting at least one biomarker from Table 17, at least one biomarker from Table 18, and at least one biomarker from Table 19. It is to be understood that these methods also include steps (b) and (c) as described above.

It is important to understand that the various biomarkers to be measured in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Measurement”.

C. Women's Group

The present invention also provides a method for designing a kit for assisting in the diagnosis of lung disease in female subjects, comprising (a) selecting at least one biomarker from Table 9A or 20A; (b) selecting a tool for measuring the expression level of said at least one biomarker; and (c) designing a kit comprising said tool for measuring the expression level.

The present invention also provides a method for designing a kit for diagnosing non-small cell lung cancer or reactive airway disease in female subjects, comprising (a) selecting at least one biomarker from Table 9B or 20B; (b) selecting a tool for measuring the expression level of said at least one biomarker; and (c) designing a kit comprising said tool for measuring the expression level.

The present invention also provides a method for designing a kit for diagnosing non-small cell lung cancer or reactive airway disease in female subjects, comprising (a) selecting at least one biomarker from Table 9C or 20C; (b) selecting a tool for measuring the expression level of said at least one biomarker; and (c) designing a kit comprising said tool for measuring the expression level.

The present invention also provides a method for designing a kit for diagnosing reactive airway disease in female subjects, comprising (a) selecting at least one biomarker from Table 10 or 21; (b) selecting a tool for measuring the expression level of said at least one biomarker; and (c) designing a kit comprising said tool for measuring the expression level.

The present invention also provides a method for designing a kit for diagnosing non-small cell lung cancer in female subjects, comprising (a) selecting at least one biomarker from Table 11 or 22; (b) selecting a tool for measuring the expression level of said at least one biomarker; and (c) designing a kit comprising said tool for measuring the expression level.

The present invention also provides a method for designing a kit for assisting in the diagnosis of lung disease in female subjects, comprising (a) selecting at least one biomarker from Table 12 or 23; (b) selecting a tool for measuring the expression level of said at least one biomarker; and (c) designing a kit comprising said tool for measuring the expression level.

In the method described above, steps (b) and (c) may alternatively be performed by: (b) selecting a detection reagent for detecting the at least one biomarker and (c) designing a kit comprising the detection reagent for detecting the at least one biomarker.

The present invention also provides a method for designing a reagent kit, comprising selecting at least one biomarker from more than one table. For example, the present invention provides a method for designing a reagent kit comprising selecting at least one biomarker from Table 10 and at least one biomarker from Table 11. In another example, the present invention provides a method for designing a reagent kit comprising selecting at least one biomarker from Table 10, at least one biomarker from Table 11, and at least one biomarker from Table 12. In another example, the present invention provides a method for designing a reagent kit comprising selecting at least one biomarker from Table 21, at least one biomarker from Table 22, and at least one biomarker from Table 23. It is to be understood that these methods also include steps (b) and (c) as described above.

It is important to understand that the various biomarkers to be measured in these specific methods can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Measurement”.

Reagent test kit

This invention provides a kit comprising a method for determining the expression level of at least one biomarker described herein. This invention also provides a kit comprising detection reagents for detecting at least one biomarker described herein.

This invention provides a kit comprising a tool for determining the expression level of at least one biomarker from Table 1A. This invention also provides a kit comprising a detection reagent for detecting at least one biomarker from Table 1A.

The present invention also provides a kit comprising a method for determining the expression level of SEQ ID NO: 12. In one embodiment, the kit comprises a tool for determining the expression level of SEQ ID NO: 12 and any combination of SEQ ID NO: 1-11 and 13-17.

The present invention also provides a kit comprising a detection reagent for detecting SEQ ID NO: 12. In one embodiment, the kit comprises a detection reagent for detecting SEQ ID NO: 12 and any combination of SEQ ID NO: 1-11 and 13-17.

The present invention also provides a kit comprising a tool for determining the expression level of at least one polypeptide selected from SEQ ID NO: 1-17 and a tool for determining the expression level of at least one biomarker from Table 1A.

The present invention also provides a kit comprising a detection reagent for detecting at least one polypeptide selected from SEQ ID NO: 1-17 and a detection reagent for detecting at least one biomarker from Table 1A.

The implementation described above refers to the biomarkers in Table 1A. However, it should be understood that the biomarkers in Table 1A can be replaced with biomarkers from Tables 1B, 1C, 2, 3, 4, 5A, 5B, 5C, 6, 7, 8, 9A, 9B, 9C, 10, 11, 12, 16A, 16B, 16C, 17, 18, 19, 20A, 20B, 20C, 21, 22, or 23 in any of the described kits.

The present invention also provides a kit comprising (a) a first tool for determining the expression level of at least one biomarker from Table 2; and (b) a second tool for determining the expression level of at least one biomarker from Table 3, wherein the at least one biomarker from Table 2 and Table 3 is not the same.

The present invention also provides a kit comprising (a) a detection reagent for detecting at least one biomarker from Table 2; and (b) a detection reagent for detecting at least one biomarker from Table 3, wherein the at least one biomarker from Table 2 and Table 3 are not the same.

The present invention also provides a kit comprising (a) a first tool for determining the expression level of at least one biomarker from Table 2; (b) a second tool for determining the expression level of at least one biomarker from Table 3; and (c) a third tool for determining the expression level of at least one biomarker from Table 4, wherein the at least one biomarker from Tables 2, 3 and 4 is not the same.

The present invention also provides a kit comprising (a) a detection reagent for detecting at least one biomarker from Table 2; (b) a detection reagent for detecting at least one biomarker from Table 3; and (c) a detection reagent for detecting at least one biomarker from Table 4, wherein the at least one biomarker from Tables 2, 3 and 4 is not the same.

The embodiments described above refer to the biomarkers in Tables 2, 3, and 4. However, it should be understood that the biomarkers in Tables 2, 3, and 4 can be replaced by the biomarkers in Tables 6, 7, 8, 17, 18, or 19 in any described kit. Furthermore, it should be understood that the biomarkers in Tables 2, 3, and 4 can be replaced by the biomarkers in Tables 10, 11, 12, 21, 22, or 23 in any described kit. Even further, those skilled in the art will understand that, for any method requiring the detection of a specific number of biomarkers, the present invention contemplates kits that include the detection of any combination of the biomarkers described above for any biomarker. It should also be understood that the multiple biomarkers to be identified as described in these specific kits can be selected from the identified tables using the criteria discussed above in the section entitled “Selection of Biomarkers for Assay.”

The following embodiments are provided to illustrate various modes of the invention disclosed herein, but are not intended to limit the invention in any way.

Example 1

Human blood samples were collected from volunteers. Thirty samples were collected from individuals who were unaware of having non-small cell lung cancer or asthma. These 30 samples are included in and referred to herein as the "normal population." Twenty-eight blood samples were collected from individuals known to have asthma and diagnosed with it by a physician. These 28 samples are included in and referred to herein as the "asthma population." Thirty blood samples were collected from individuals known to have non-small cell lung cancer and diagnosed with it by a physician. These 30 samples are included in and referred to herein as the "lung cancer population."

The study aimed to select biomarkers whose altered expression levels were believed to be associated with lung cancer or asthma. As used herein, “lung cancer” is intended to include lung cancers known to be non-small cell lung cancer. The following 59 biomarkers were selected for testing: CD40, liver growth factor (“HGF”), I-TAC (“CXCL11”; “Chemokine (C-X-C motif) ligand 11”, “Interferon-induced T cell α-chemical attractant”), leptin (“LEP”), matrix metalloproteinases (“MMP”) 1, MMP2, MMP3, MMP7, MMP8, MMP9, MMP12, MMP13, CD40 soluble ligand (“CD40 ligand”), epidermal growth factor (“EFG”), eosinophil activity... Chemokinesin (“CCL11”), Fractalkine, granulocyte colony-stimulating factor (“G-CSF”), granulocyte-macrophage colony-stimulating factor (“GM-CSF”), interferon-γ (“IFNγ”), interleukin (“IL”) 1α, IL-1β, IL-1ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40), IL-12 (p70), IL-13, IL-15, IL-17, IP-10, Monocyte chemotactic protein 1 (“MCP-1”), macrophage inflammatory protein (“MIP”) 1α, MIP-1β, transforming growth factor α (“TGFα”), tumor necrosis factor α (“TNFα”), vascular endothelial growth factor (“VEGF”), auxin (“Ins”), C-peptide, glucagon-like protein-1/amylin (“GLP-1/amylin”), amylin (total), glucagon, adiponectin, plasminogen activator inhibitor 1 (“PAI-1”; “serine protein”) Enzyme inhibitors ("Active/Total"), resistin ("RETN"; "xcp1"), sFas, soluble Fas ligand ("sFasL"), macrophage migration inhibitory factor ("MIF"), sE-selectin, soluble vascular cell adhesion molecule ("sVCAM"), soluble intracellular adhesion molecule ("sICAM"), myeloperoxidase ("MPO"), C-reactive protein ("CRP"), serum amyloid A ("SAA"; "SAA1"), and serum amyloid P ("SAP").

Plasma samples were analyzed using Luminex’s xMAP technology (a quantitative multiplex immunoassay using an automated bead-based technique) to screen plasma samples from each of the 59 biomarkers in normal, asthmatic, and lung cancer populations.

Several different assay kits are used with Luminex xMAP technology to screen for biomarkers, namely Millipore’s Human Cytokine/Chemokine (Cat#MPXHCYTO-60K), Human Endocrine (Cat#HENDO-65K), Human Serum Adipokines (Cat#HADKI-61K), Human Sepsis/Apoptosis (Cat#HSEP-63K), Human Cardiovascular Panel 1 (Cat#HCVD1-67AK) and Human Cardiovascular Panel 2 (HCVD2-67BK), R&D Systems, Inc.’s Human Fluorokine MAP Profiling Base Kit B (Cat#LUB00) and Human Fluorokine MAP MMP Profiling Base Kit (Cat#LMP000). For each plasma sample from each population, the fluorescence intensity level of each of the 59 biomarkers was recorded using a combined immunoassay. The recorded fluorescence intensity was proportional to the concentration of the corresponding biomarker in the sample and to its expression level in the individual. The mean, standard deviation, and relative standard deviation of the fluorescence intensity levels associated with each biomarker were calculated for each population. Figures 1A-1C show the mean, standard deviation, and relative standard deviation of each biomarker in the normal (NO), non-small cell lung cancer (LC), and asthma (AST) populations.

The Student's t-test was then used to qualitatively assess the inter-pathological differences for each specific biomarker across the populations. Mean fluorescence intensity measurements of each biomarker from samples of healthy patients were compared to those from samples of patients with lung cancer, and also to those derived from samples of patients with asthma. Figure 1D illustrates the differences for each biomarker across multiple populations. Furthermore, the significance of comparing the mean fluorescence intensity measurements of lung cancer patients with those of asthma patients was estimated using the Student's t-statistic.

Further analysis was conducted to examine the statistical differences in each biomarker among the normal, asthmatic, and lung cancer populations. To qualitatively assess the differences in mean expression levels of each biomarker across the populations, the Student's t-value was calculated using the t-test function available in the Microsoft Excel software package. Assuming equal variances in the two-tailed assignments, the probability associated with the Student's t-value was calculated using the Excel t-test function.

The significance of differences in expression levels between groups was determined by the following criteria: any student t-value with a correlation probability less than 0.05 was considered significant and indicative of the presence of a given pathological condition, whether asthma or lung cancer. The use of a criterion of 0.05 or lower is generally accepted in the scientific community. Any student t-value with a correlation probability greater than 0.1 was considered insignificant and could not indicate the presence of a given pathological condition. Furthermore, any student t-value with a correlation probability between 0.05 and 0.1 was determined to be marginally significant.

Referring to Figure 1E, the student t-values with the calculated correlation probabilities for each biomarker across different groups are shown. It should be noted that the student t-values with the correlation probabilities shown in Figure 1E are calculated based on the assumption that the asthma, normal, and lung cancer groups have a single mean and a normal distribution.

The significance of differences in biomarker expression levels was used to rank the relative importance of biomarkers. Biomarkers found to differ significantly across pathological states were classified as relatively more important. Measurements of mean fluorescence intensity were examined, and data for biomarkers whose intensities did not deviate significantly from the mean intensity of other population samples were excluded from further analysis. Biomarkers with relatively low relative standard deviations were classified as more significant than those with relatively high standard deviations.

The relative significance of a specific biomarker is evaluated without considering the direction of bias (i.e., whether the average level of the specific biomarker is increasing or decreasing relative to any other pathological state). In this way, a set of biomarkers exhibiting high variability, relatively low relative standard deviation, and good instrument detectability (defined as non-zero uncorrected mean fluorescence intensity) across different pathological states is assembled. These calculations are used to test the power of immunoassays and, through analysis, to identify biomarkers showing significant differences in expression levels between normal populations, and also to determine reference ranges characteristic of or relevant to pathological states of lung cancer and/or asthma.

Referring again to Figure 1E, probabilities associated with Student's t-values were calculated to compare the asthma and control groups. Significant differences between the asthma and control groups were determined from the Student's t-probabilities of the biomarkers sE-selectin, EGF, leptin, IL-5, PAI-1, resistin, MMP-13, CD40 ligand sVCAM-1, HGF, C-peptide, sICAM-1, MMP-7, adiponectin, GM-CSF, and MIF. This determination was based on the fact that the Student's t-probability of each of these biomarkers was less than 0.05 when comparing 28 samples from the asthma group with 30 samples from the control group using the Student's t-function described herein. Differences between the asthma and control groups were determined to be insignificant when the Student's t-probability of each of the biomarkers CRP, MMP-9, IL-4, IL-1α, SAA, IL-7, and IL-6 was significantly greater than 0.05.

As also shown in Figure 1E, probabilities associated with the Student's t-value were calculated to compare the lung cancer population with the normal population. Significant differences between the lung cancer and normal populations were determined from the Student's t-probabilities of the biomarkers sE-selectin, EGF, leptin, IL-5, PAI-1, resistin, CRP, MMP-9, IL-4, IL-1α, SAA, IL-7, CD40 ligand, MMP-7, and MMP-12. Again, this determination was based on the Student's t-probability of each of these biomarkers being less than 0.05 when comparing 30 samples from the lung cancer population with 30 samples from the normal population using the Student's t-function described herein. Differences between the lung cancer and normal populations were determined to be insignificant when the Student's t-probability of each of the biomarkers MMP-13, HGF, C-peptide, sICAM, adiponectin, GM-CSF, IL-17, TNFα, ITAC, and MIF was significantly greater than 0.05.

The probability of each of the three biomarkers being associated with a Student's t-value between the lung cancer and normal populations was only slightly greater than 0.05. Specifically, when comparing the lung cancer and normal populations, the Student's t-probability for IL-6 was 0.076195528, for sVCAM-1 it was 0.08869949, and for IL-15 it was 0.086324372. These biomarkers were considered not significantly different between the lung cancer and normal populations. However, given that the Student's t-probabilities for these three biomarkers are close to 0.05, it is possible that each biomarker varies significantly between the normal and lung cancer populations.

Finally, as shown in Figure 1E, the lung cancer and asthma groups were compared by calculating the Student's t-probability correlation. Significant differences between the lung cancer and asthma groups were determined from the Student's t-probabilities of the biomarkers sE-selectin, EGF, leptin, IL-5, PAI-1, resistin, CRP, MMP-9, IL-4, IL-1α, SAA, IL-7, IL-6, MMP-13, sVCAM, HGF, C-peptide, sICAM, adiponectin, GM-CSF, IL-17, IL-15, TNFα, and I-TAC. This determination was based on the Student's t-probability correlation of less than 0.05 for each of these biomarkers when comparing 30 samples from the lung cancer group with 28 samples from the asthma group using the Student's t-function described herein. Differences were considered insignificant when the Student's t-probability of each of the biomarkers CD40 ligand, MMP-7, MMP-12, and MIF between the lung cancer and asthma groups was significantly greater than 0.05.

Example 2

Human blood samples were collected from volunteers. 142 samples were collected from individuals who were unaware of having non-small cell lung cancer or asthma. These samples are included in and referred to herein as the “normal population.” 108 blood samples were collected from individuals known to have asthma and diagnosed with it by a physician. These samples are included in and referred to herein as the “asthma population.” 146 blood samples were collected from individuals known to have non-small cell lung cancer and diagnosed with it by a physician. These samples are included in and referred to herein as the “lung cancer population.”

The same method as described in Example 1 was performed. Figures 2A-2E show the results obtained. These results provide guidance for selecting suitable biomarkers for the methods of the present invention. Specifically, the probability values of specific markers are useful at this point.

Figure 2E shows the probabilities associated with the effectiveness of various biomarkers used to distinguish the physiological states of different populations. Probability values of 0.1 or less are highlighted in this table to identify target biomarkers. The biomarker probability values used in the preferred method of this invention are 0.05 or less, more preferably 0.01, and even more preferably 0.001 or less.

Example 3

Human blood samples were collected from volunteers. 288 samples were collected from individuals who were unaware of having non-small cell lung cancer or asthma. These samples are included in and referred to herein as the "normal population." 180 blood samples were collected from individuals known to have asthma and diagnosed with it by a physician. These samples are included in and referred to herein as the "asthma population." 360 blood samples were collected from individuals known to have non-small cell lung cancer and diagnosed with it by a physician. These samples are included in and referred to herein as the "lung cancer population."

The same method as described in Example 1 was performed. Panomics' Procarta Cytokine Kit (Cat#PC1017) was also used. Antibodies for PAI-1 and leptin were used from two different kits. Antibodies for PAI-1 ^A and leptin ¹ were produced by Millipore. The antibody for PAI-1 ^B was produced by Panomics. Figures 3A-3E show the results obtained. These results provide guidance for selecting biomarkers suitable for the methods of this invention. Specifically, the probability values of specific markers are useful in this regard.

Figure 3E shows the probabilities associated with the effectiveness of various biomarkers used to distinguish the physiological states of different populations. Probability values of 0.1 or less are highlighted in this table to identify target biomarkers. The biomarker probability values used in the preferred method of the present invention are 0.05 or less, more preferably 0.01, and even more preferably 0.001 or less.

The data is then aggregated and analyzed by gender.

Figures 4A-4C show the mean fluorescence intensity levels of biomarkers in women with normal (NO), non-small cell lung cancer (LC), and asthma (AST). Figure 4D shows the percentage change in the mean of each biomarker in women with AST relative to NO, LC relative to NO, and AST relative to LC. Figure 4E shows the probability associated with the Student's t-value calculated by comparing the mean fluorescence intensity of each biomarker measured, where the mean values to be compared are AST relative to NO, LC relative to NO, and AST relative to LC.

The same information regarding the male population is shown in Figures 5A-5E.

Next, the data from the female and male groups were compared. Figure 6A shows the percentage change in the mean values of each biomarker in the AST male group compared to the AST female group, the LC male group compared to the LC female group, and the NO male group compared to the NO female group. Figure 6B shows the probability associated with the Student's t-value calculated by comparing the mean fluorescence intensity of each biomarker measured in the male and female groups from Example 3, where the mean values to be compared are for the AST male and female groups, the LC male and female groups, and the NO male and female groups, respectively.

Example 4

The Kruskal-Wallis test is a well-known nonparametric statistical method. Data from Example 3 were pooled according to gender and analyzed using the Kruskal-Wallis (U test). Markers with a probability value of 0.05 or less were considered significant. Markers showing marginally significant differences (probabilities between 0.051 and 0.10) and insignificant differences (probabilities above 0.10) were discarded. The results for the retained markers are shown in Figures 7-8.

Figure 7A shows the percentage change in the mean concentration of each biomarker relative to the NO female population, the AST female population relative to the NO female population, and the AST female population relative to the LC female population. Pure sums (i.e., the sum of the absolute values of the percentage changes in all three comparisons) are also provided and used to rank the biomarkers. Figure 7B shows the probabilities associated with the Kruskal-Wallis test calculated by comparing the concentrations of each biomarker, where the populations to be compared are the AST female population relative to the NO female population, the LC female population relative to the NO female population, and the AST female population relative to the LC female population.

The same information regarding the male population is shown in Figures 8A and 8B.

Biomarkers exhibited unique sex- and disease-specific patterns. In single-sex analysis of LC, 36 markers with cutoff thresholds of at least 25% absolute change and 32 markers with cutoff thresholds of at least 50% were identified. For women, 32 markers with at least 25% cutoff and 30 with at least 50% cutoff were found. For men, 39 markers with at least 25% cutoff and 37 with at least 50% cutoff were found. In women, the expression of four markers was unique in LC compared to NO: IL-8 with serum amyloid P (downregulated), serum amyloid A and C-reactive protein (both upregulated). In men, the expression of five markers was unique in LC compared to NO: auxin (downregulated), matrix metalloproteinases-7 and -8, resistin, and liver growth factor (both upregulated). The three markers showed opposite expression patterns: (i) VEGF was downregulated in women and upregulated in men in LC compared to NO; (ii) leptin was upregulated in women and downregulated in men; and (iii) MIP-1a was upregulated in men and downregulated in women in LC compared to NO.

This invention provides various methods for identifying disease states based on gender. For example, this invention provides methods for physiological qualitative analysis in male subjects, including determining whether growth hormone is downregulated and/or whether matrix metalloproteinases-7 and-8, resistin, and liver growth factor are upregulated. These patterns indicate disease. The assays included in this invention involve detecting abnormal up/down regulation of three, four, or five of these biomarkers in male subjects.

In another instance, the present invention provides a method for physiological qualitative analysis in female subjects, including determining whether IL-8 and/or serum amyloid P are downregulated, and/or serum amyloid A and C-reactive proteins are upregulated. These forms indicate disease. The assays included in the present invention involve detecting abnormal up/down regulation of three or four of these biomarkers in female subjects.

Example 5

Human blood samples were collected from volunteers. Thirty samples were collected from individuals who were not known to have non-small cell lung cancer or asthma. These individuals who were not known to have non-small cell lung cancer or asthma are included and referred to herein as the “normal population.” Twenty-eight blood samples were collected from individuals who were known to have asthma and were diagnosed with it by a physician. These individuals who were known to have asthma are included and referred to herein as the “asthma population.” Thirty blood samples were collected from individuals who were known to have non-small cell lung cancer and were diagnosed with it by a physician. These individuals who were known to have non-small cell lung cancer are included and referred to herein as the “lung cancer population.” Generally, as used herein, the term “lung cancer” or “multiple lung cancers” refers to non-small cell lung cancer.

Eight to ten plasma samples from each of the asthma, healthy, and lung cancer groups were randomly selected for testing. Each sample from each group was treated with a protease or digestive reagent. Trypsin was used as the protease, and its ability to produce highly specific and predictable cleavage (due to its known ability to hydrolyze peptide chains at the carboxyl terminus of lysine and arginine unless proline is immediately present thereafter) is highly desirable. Other proteases or digestive reagents may also be used, although trypsin may be employed. It is desirable to use a protease or mixture of proteases that are at least as specific as trypsin in hydrolysis.

The trypsin peptides (peptides formed after trypsin cleavage) were then separated from insoluble substances by centrifugation and capillary liquid chromatography using a water-acetonitrile gradient of 0.1% formic acid. A 0.375 x 180 mm Supelcosil ABZ+ column was used on an Eksigent 2D capillary HPLC system to achieve the chromatographic resolution of the produced trypsin peptides. Peptide separation is necessary because the electrospray ionization process is subject to ion co-suppression, where ions with higher proton affinity suppress the formation of ions with lower proton affinity when simultaneously eluted from the electrospray emitter. In this case, the electrospray emitter and the end of the HPLC column are co-terminated.

This methodology enables the chromatographic separation of the large number of peptides produced during trypsin digestion and helps minimize co-inhibition problems, thus maximizing the probability of pseudo-molecular ion co-suppression and thereby maximizing ion sampling. The trypsin peptides of each sample are then subjected to LC-ESIMS. LC-ESIMS promptly separates the individual peptides in each sample by passing them through a column containing a solvent system of water, acetonitrile, and formic acid as described above.

The peptide is then sprayed using an electrospray ionization source to ionize it and generate peptide pseudomolecules as described above. The peptide is then passed through a mass spectrometer in an LC-ESIMS, where the molecular weight of each peptide pseudomolecule is measured. After passing through the LC-ESIMS, mass spectrometry readings are generated from the mass spectrometry data for each peptide present in the sample, namely the peptide's intensity, molecular weight, and elution time. The mass spectrometry readings are typically graphical representations of the peptide pseudomolecule signals recorded by the LC-ESIMS, where the x-axis represents the measured mass-to-charge ratio and the y-axis represents the signal intensity of the pseudomolecule signal. These data are then processed by the software system controlling the LC-ESIMS to obtain and store the resulting data.

Once mass spectrometry data are acquired and plotted on the mass spectrometer, comparative analyses are performed, including interpathologically and intrapathologically mass spectrometry readings of each serum sample tested in LC-ESIMS for each population. Mass spectrometric peaks are compared between samples tested in the normal population. Then, mass spectrometric peaks are compared between samples tested in the asthma and lung cancer populations. After intrapathological comparisons, interpathological comparisons are performed, where mass spectrometry readings of each sample tested in LC-ESIMS for the asthma population are compared with those for samples tested in the normal population. Similarly, mass spectrometry readings of each sample tested in LC-ESIMS for the lung cancer population are compared with those for samples tested in the normal population.

Peptides with the following mass spectrometry readings are identified as not significant and excluded: when comparing asthma or lung cancer populations with normal populations, the mass spectrometry readings indicate inconsistently differential expression of the peptide intensity within the pathological state or substantially no change (less than 10-fold variance in intensity). Typically, the exclusion criteria used include: for a given protein, comparing the peak intensities of at least half of the identified characteristic peptides across at least 10 datasets derived from serum samples from individual patients of each pathological state. If the intensity of most peptide peaks derived from a given protein is at least 10-fold higher in intensity for 80% of the serum datasets, the protein is classified as differentially modulated between the two pathological state categories.

However, the characteristics of proteins that produce peptides that are observed to be differentially regulated are unknown and require identification. To identify these proteins, the intensity of the peptide pseudomolecule ion signal is compared across a known database containing libraries of known proteins and peptides as well as suspected proteins and peptides.

Mass spectrometry readings of trypsin digests from various samples from different normal, lung cancer, and asthma groups were input into a known search engine called Mascot. Mascot is a search engine known in the art that uses mass spectrometry data to identify proteins from four major sequencing databases: MSDB, NCBInr, SwissProt, and dbEST. These databases contain information on all proteins with known sequences and all putative proteins based on observations of characteristic protein transcription start regions derived from gene sequences. These databases are continuously checked for accuracy and redundancy, and new proteins and gene sequences are continuously identified and published in scientific and patent literature.

The search criteria and parameters are entered into the Mascot program, and mass spectrometry data from mass spectrometry readings from each population are run through the Mascot program. The mass spectrometry data entered into the Mascot program are used for all samples from each pathological state. The Mascot program then runs the mass spectrometry data of the entered peptides against the sequencing database, comparing the peak intensity and mass of each peptide with the mass and peak intensity of known peptides and proteins. Mascot then generates search results, returning a list of candidates for possible protein identification matches (often referred to as “significant matches” for each sample analyzed).

Significant matches are determined by the Mascot procedure using a score called the “Mowse score” assigned to each tested sample. The Mowse score is an algorithm where the score is -10 * LOG ₁₀ (P), where P is the probability that the observed match is a random event, correlated with a significance p-value where p is less than 0.05, which is the generally accepted standard in the scientific community. Mowse scores of approximately 55 to approximately 66 or higher are generally considered significant. Significance levels may vary slightly due to specific search considerations and database parameters. Running the procedure for each peptide returns the significant matches, generating a list of protein candidates.

Next, a comparative analysis is performed using the same methods described in US20090069189, which is incorporated herein by reference in its entirety.

Data from the mass spectrometry readings are cross-checked against the significant matches to validate the raw data, peak characteristics, charge multiplicity, isotopic distribution, and flanking charge states. A reverse search is then performed to add peptides to the candidate list that might have been missed by the Mascot program's automated search. Added peptides are identified by selecting the "best match"—a single protein whose parameters substantially match those of the peptide being compared—and performing a computer digestion, where the trypsin peptide and its respective molecular mass are calculated based on the protein's known amino acid or gene sequence. These predicted peptide masses are then searched against the raw mass spectrometry data, and any identified peaks are checked and qualified as described above. All peptides, including those automatically identified by Mascot and those identified through manual check, are then added to the mass list used by Mascot. As described below, the refined matches are then used to derive a refined Mowse score.

As a result of the identification process, protein arginase-1 was determined to have significantly differential expression among asthma, lung cancer, and/or normal populations. Other proteins identified using this method include BAC04615, Q6NSC8, CAF17350, Q6ZUD4, Q8N7P1, CAC69571, protein 4 containing the FERM domain, the C2 long splice form of the JCC1445 proteasome endopeptidase complex chain, synaptic fusion protein 11, AAK13083, and AAK130490. Comparative analyses using the same method described in US20090069189 are incorporated herein by reference in their entirety.

Specific proteins persistently differentially expressed in patients with asthma and lung cancer have been identified, making it possible to diagnose these pathological conditions early in disease progression by trying proteins in patient plasma and analyzing them via LC-ESIMS to obtain mass spectrometry data. The analysis would then determine whether the mass spectrometry data includes peaks of one or more of the following: arginase-1, BAC04615, Q6NSC8, CAF17350, Q6ZUD4, Q8N7P1, CAC69571, protein 4 containing the FERM domain, the C2 long splice form of the proteasome endopeptide complex JCC1445, synaptic fusion protein 11, AAK13083, and AAK130490. The levels of any proteins found in patient samples would then be compared to levels found in the normal population.

The amino acid sequence disclosed in SEQ ID NO:1 is the primary amino acid sequence of protein BAC04615, known as of the date of filing of this application. The amino acid sequence disclosed in SEQ ID NO:2 is the primary amino acid sequence of protein Q6NSC8, known as of the date of filing of this application. The amino acid sequence disclosed in SEQ ID NO:3 is the primary amino acid sequence of protein CAF17350, known as of the date of filing of this application. The amino acid sequence disclosed in SEQ ID NO:4 is the primary amino acid sequence of protein Q6ZUD4, known as of the date of filing of this application. The amino acid sequence disclosed in SEQ ID NO:5 is the primary amino acid sequence of protein 4, which contains the FERM domain, known as of the date of filing of this application. The amino acid sequence disclosed in SEQ ID NO:6 is the primary amino acid sequence of protein AAK13083, known as of the date of filing of this application. The amino acid sequence disclosed in SEQ ID NO:7 is the primary amino acid sequence of protein Q8N7P1, known as of the date of filing of this application. The amino acid sequence disclosed in SEQ ID NO:8 is the primary amino acid sequence of protein CAC69571, known as of the date of filing this application. The amino acid sequence disclosed in SEQ ID NO:9 is the primary amino acid sequence of the C2 long splice of the proteasome endopeptide complex chain of protein JCC1445, known as of the date of filing this application. The amino acid sequence disclosed in SEQ ID NO:10 is the primary amino acid sequence of synaptic fusion protein 11, known as of the date of filing this application. The amino acid sequence disclosed in SEQ ID NO:11 is the primary amino acid sequence of protein AAK13049, known as of the date of filing this application. The amino acid sequence disclosed in SEQ ID NO:12 is the primary amino acid sequence of arginase-1, known as of the date of filing this application.

Example 6

The selected tissue samples from asthma patients were subjected to the same method described in Example 5. See also application number 61/176,437, which is incorporated herein by reference in its entirety.

As a result of the identification process, the following proteins were identified as differentially expressed significantly in asthma patients:

Five specific proteins that are persistently differentially expressed in asthma patients have been identified, making it possible to diagnose these pathological conditions early in disease progression by trying the proteins in patient tissue plasma and obtaining mass spectrometry data via LC-ESIMS analysis, and determining whether the mass spectrometry data includes one or more peaks of SEQ ID NO: 13-17. The levels of any proteins found in patient samples are then compared to levels found in the normal population.

Example 7

Diagnostic tests for non-small cell lung cancer

Biological fluid samples are obtained from patients from whom diagnostic information is required. Samples are preferably serum or plasma. The concentrations of seven of the following 14 biomarkers are determined in the sample: IL-13, I-TAC, MCP-1, MMP-1, MPO, HGF, eosinophil activation chemokine, MMP-9, MMP-7, IP-10, SAA, resistin, IL-5, and sVACM-1. The concentrations of each biomarker measured from the sample are compared to the concentration ranges of that biomarker found in the same fluid in healthy individuals, a group of individuals diagnosed with asthma, a group of individuals diagnosed with asthma, and a group of individuals diagnosed with NSCLC. Deviation from the normal range indicates lung disease, and deviation from the range in the group of individuals diagnosed with asthma indicates NSCLC. Testing patients with biomarkers from the same group of 14 biomarkers can be used with similar procedures for the diagnosis of asthma or other reactive airway diseases.

Example 8

Monitoring the treatment of non-small cell lung cancer

Pre-treatment samples of biological fluids were obtained from patients diagnosed with NSCLS prior to any treatment of the disease. Samples were preferably serum or plasma. The concentrations of eight of the following 24 biomarkers were determined in the samples: IL-13, EGF, I-TAC, MMP-1, IL-12 (p70), eosinophil activation chemokine, MMP-8, MCP-1, MPO, IP-10, SAA, HGF, MMP-9, MMP-12, total amyloidin, MMP-7, IL-6, MIL-1β, adiponectin, IL-10, IL-5, IL-4, SE-selectin, and MIP-1α. The concentrations of each biomarker measured from the samples were compared to the range of concentrations of that marker found in the same fluids in healthy individuals. After obtaining the pre-treatment samples, the patient underwent a therapeutic intervention, including surgery followed by radiotherapy. Samples of the same fluids were obtained after surgery but before radiotherapy. Additional samples were obtained after each radiotherapy session. The concentrations of the same eight biomarkers in each sample were determined. Changes in the expression levels of each biomarker were recorded and compared with other symptoms of disease progression.

Example 9

Select predictive biomarkers

Pre-treatment samples of biological fluids were obtained from patients diagnosed with NSCLS prior to any treatment of the disease. Samples were preferably serum or plasma. The concentrations of the following 24 biomarkers were determined in the samples: IL-13, EGF, I-TAC, MMP-1, IL-12 (p70), eosinophil activation chemokine, MMP-8, MCP-1, MPO, IP-10, SAA, HGF, MMP-9, MMP-12, total amyloidin, MMP-7, IL-6, MIL-1β, adiponectin, IL-10, IL-5, IL-4, SE-selectin, and MIP-1α. The concentrations of each biomarker measured from the samples were compared to the range of concentrations of that biomarker found in the same fluids of healthy individuals. After obtaining the pre-treatment samples, the patient underwent a therapeutic intervention, including surgery, followed by radiotherapy. Samples of the same fluids were obtained after surgery but before radiotherapy. Additional samples were obtained after each radiotherapy session. Determine the concentrations of these 24 biomarkers in each sample. Record changes in the expression levels of each biomarker and compare them with other symptoms of disease progression. Identify all biomarkers whose levels changed after treatment.

Example 10

Select suspected patients

Biological fluid samples were obtained from patients already diagnosed with NSCLS. The samples were preferably serum or plasma. The concentrations of each biomarker identified in the preceding examples were determined in the samples, and patients showing the largest number of biomarkers deviating from normal values were selected for treatment.

Example 11

Diagnostic test for non-small cell lung cancer in male subjects

Samples of biological fluids are obtained from male patients requiring diagnostic information. Samples are preferably serum or plasma. The concentrations of seven of the following 14 biomarkers are determined in the sample: I-TAC, MPO, HGF, MMP-1, MMP-8, eosinophil activation chemokine, IL-8, MMP-7, IP-10, sVACM-1, IL-10, adiponectin, SAP, and IFN-γ. The concentrations of each biomarker measured from the sample are compared to the concentration ranges of the same biomarker found in the same fluids of normal male individuals, a group of male individuals diagnosed with asthma, and a group of male individuals diagnosed with NSCLC. Deviation from the normal range indicates lung disease, and deviation from the range in the group of asthmatic individuals indicates NSCLC. Testing patients using biomarkers from the same group of 14 can be used with similar procedures for the diagnosis of asthma or other reactive airway diseases.

Example 12

Candidate non-small cell lung cancer tests in male subjects

Many (if not all) of the biomarkers identified in Tables 1-15 are involved in the communication pathways described above. Some of these biomarkers are correlated with each other as first-level interactors. The known relationships between specific biomarkers and their first-level interactors facilitate the selection of markers for diagnostic or predictive assays. The known communication relationships between biomarkers listed in Table 16B are shown in Figure 9, generated using the Ariadne system. Figure 9 shows the first-level interactors of HGF (liver growth factor), including sFasL (soluble Fas ligand), PAI-1 (serine protease inhibitor 1) (activity/total), Ins (auxin; which also includes C-peptide), EGF (epidermal growth factor), MPO (myeloperoxidase), and MIF (macrophage migration inhibitory factor). Other interactors (non-first-order) include RETN (resistin, xcp1), SAA1 (serum amyloid A, SAA), CCL11 (eosinophil activation chemokine), LEP (leptin), and CXCL11 (chemokine (C-X-C domain) ligand 11, interferon-induced T cell α-chemical attractant (I-TAC) or interferon-γ-inducible protein 9 (IP-9)). Furthermore, Figure 9 shows that two biomarkers, MMP1 and MMP-8 (matrix metalloproteinases 1 and 8), are not in the same communication pathway as HGF.

One way to maximize the information gathered by measuring a selected set of biomarkers is to select a variety of biomarkers such that the set includes biomarkers that are not in the same communication pathway. Using a column of biomarkers in Table 16B, it appears that if the levels of at least HGF or another biomarker of the HGF first-order interactor and MMP-8 are abnormal in male subjects, the subject is much more likely to have lung cancer. If the level of MMP-1 is also abnormal, the likelihood is even higher. Therefore, the method according to the invention for diagnosing lung cancer in male subjects is to measure the levels of at least HGF or another biomarker of the HGF first-order interactor and MMP-8 and compare them to the range expected for a normal population to see if the levels of these biomarkers are abnormal. In a preferred mode, the diagnostic method also includes measuring whether the level of MMP-1 is normal. More preferably, one or more of CXCL11, LEP, SAA1, and/or RETN are also measured, with their levels compared to the range expected for a normal population of individuals. The more of these biomarkers present at abnormal levels, the more likely the subject is to have lung cancer.

Example 13

Monitoring treatment for non-small cell lung cancer in men

Pre-treatment samples of biological fluids were obtained from male patients diagnosed with NSCLS prior to any treatment of the disease. Samples were preferably serum or plasma. The concentrations of eight of the following 24 biomarkers were determined in the samples: IL-13, I-TAC, EGF, MPO, HGF, MMP-1, MMP-8, MIF, eosinophil activation chemokine, IL-12 (p70), MCP-1, MMP-9, SAA, IP-10, total amyloidin, MMP-7, resistin, IL-6, MIP-1β, TNFα, IL-8, IL-5, CRP, and IL-10. The concentrations of each biomarker measured from the samples were compared to the range of concentrations of that marker found in the same fluids in healthy individuals. After obtaining the pre-treatment samples, the patient underwent a therapeutic intervention, including surgery, followed by radiotherapy. Samples of the same fluids were obtained after surgery but before radiotherapy. Additional samples were obtained after each radiotherapy session. Determine the concentrations of the same eight biomarkers in each sample. Record changes in the expression levels of each biomarker and compare them with other symptoms of disease progression.

Example 14

Select predictive biomarkers

Pre-treatment samples of biological fluids were obtained from patients diagnosed with NSCLS prior to any treatment of the disease. Samples were preferably serum or plasma. The concentrations of the following 24 biomarkers were determined in the samples: IL-13, I-TAC, EGF, MPO, HGF, MMP-1, MMP-8, MIF, eosinophil activation chemokine, IL-12 (p70), MCP-1, MMP-9, SAA, IP-10, total amyloidin, MMP-7, resistin, IL-6, MIP-1β, TNF-α, IL-8, IL-5, CRP, and IL-10. The concentrations of each biomarker measured from the samples were compared to the range of concentrations of that biomarker found in the same fluids of healthy individuals. After obtaining the pre-treatment samples, the patient underwent a therapeutic intervention, including surgery, followed by radiotherapy. Samples of the same fluids were obtained after surgery but before radiotherapy. Additional samples were obtained after each radiotherapy session. Determine the concentrations of these 24 biomarkers in each sample. Record changes in the expression levels of each biomarker and compare them with other symptoms of disease progression. Identify all biomarkers whose levels changed after treatment.

Example 15

Select suspected patients

The biological fluid samples were obtained from patients already diagnosed with NSCLS. The samples were preferably serum or plasma. The concentrations of each biomarker identified in the preceding examples were determined in the samples, and patients showing the largest number of biomarkers deviating from normal values were selected for treatment.

sequence list

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<120> Methods and reagent kits for the identification, assessment, prevention, and treatment of lung diseases.

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<140> PCT/US2010/27243

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Leu Asn Glu Thr Lys Ser Gln Ala Phe Val Ser Asn Ser Pro Lys

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320 325 330

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335 340 345

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350 355 360

Pro Asp Leu Asp Ala Lys Ile Arg Glu Ala Leu Val Leu Arg Ser

365 370 375

Val Arg Val Arg Leu Leu Leu Ser Phe Trp Lys Glu Thr Asp Pro

380 385 390

Leu Thr Phe Asn Phe Ile Ser Ser Leu Lys Ala Ile Cys Thr Glu

395 400 405

Ile Ala Asn Cys Ser Leu Lys Val Lys Phe Phe Asp Leu Glu Arg

410 415 420

Glu Asn Ala Cys Ala Thr Lys Glu Gln Lys Asn His Thr Phe Pro

425 430 435

Arg Leu Asn Arg Asn Lys Tyr Met Val Thr Asp Gly Ala Ala Tyr

440 445 450

Ile Gly Asn Phe Asp Trp Val Gly Asn Asp Phe Thr Gln Asn Ala

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Gly Thr Gly Leu Val Ile Asn Gln Ala Asp Val Arg Asn Asn Arg

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Ser Ile Ile Lys Gln Leu Lys Asp Val Phe Glu Arg Asp Trp Tyr

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Ser Pro Tyr Ala Lys Thr Leu Gln Pro Thr Lys Gln Pro Asn Cys

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Ser Ser Leu Phe Lys Leu Lys Pro Leu Ser Asn Lys Thr Ala Thr

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Asp Asp Thr Gly Gly Lys Asp Pro Arg Asn Val

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Gln Asn Leu Pro Ser Ser Pro Ala Pro Ser Thr Ile Phe Ser Gly

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Gly Phe Arg His Gly Ser Leu Ile Ser Ile Asp Ser Thr Cys Thr

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Phe Ala Ile His Tyr Cys Phe Lys Thr His Ser Leu Glu Ile Cys

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Ile Lys Ala Cys Lys Asn Leu Ala Tyr Gly Glu Glu Lys Lys Lys

65 70 75

Lys Cys Asn Pro Tyr Val Lys Thr Tyr Leu Leu Pro Asp Arg Ser

80 85 90

Ser Gln Gly Lys Arg Lys Thr Gly Val Gln Arg Asn Thr Val Asp

95 100 105

Pro Thr Phe Gln Glu Thr Leu Lys Tyr Gln Val Ala Pro Ala Gln

110 115 120

Leu Val Thr Arg Gln Leu Gln Val Ser Val Trp His Leu Gly Thr

125 130 135

Leu Ala Arg Arg Val Phe Leu Gly Glu Val Ile Ile Ser Leu Ala

140 145 150

Thr Trp Asp Phe Glu Asp Ser Thr Thr Gln Ser Phe Arg Trp His

155 160 165

Pro Leu Arg Ala Lys Ala Glu Lys Tyr Glu Asp Ser Val Pro Gln

170 175 180

Ser Asn Gly Glu Leu Thr Val Arg Ala Lys Leu Val Leu Pro Ser

185 190 195

Arg Pro Arg Lys Leu Gln Glu Ala Gln Glu Gly Thr Asp Gln Pro

200 205 210

Ser Leu His Gly Gln Leu Cys Leu Val Val Leu Gly Ala Lys Asn

215 220 225

Leu Pro Val Arg Pro Asp Gly Thr Leu Asn Ser Phe Val Lys Gly

230 235 240

Cys Leu Thr Leu Pro Asp Gln Gln Lys Leu Arg Leu Lys Ser Pro

245 250 255

Val Leu Arg Lys Gln Ala Cys Pro Gln Trp Lys His Ser Phe Val

260 265 270

Phe Ser Gly Val Thr Pro Ala Gln Leu Arg Gln Ser Ser Leu Glu

275 280 285

Leu Thr Val Trp Asp Gln Ala Leu Phe Gly Met Asn Asp Arg Leu

290 295 300

Leu Gly Gly Thr Arg Leu Gly Ser Lys Gly Asp Thr Ala Val Gly

305 310 315

Gly Asp Ala Cys Ser Leu Ser Lys Leu Gln Trp Gln Lys Val Leu

320 325 330

Ser Ser Pro Asn Leu Trp Thr Asp Met Thr Leu Val Leu His

<210> 9

<211> 263

<212> PRT

<213> Homo sapiens

<400> 9

Met Phe Arg Asn Gln Tyr Asp Asn Asp Val Thr Val Trp Ser Pro

5 10 15

Gln Gly Arg Ile His Gln Ile Glu Tyr Ala Met Glu Ala Val Lys

20 25 30

Gln Gly Ser Ala Thr Val Gly Leu Lys Ser Lys Thr His Ala Val

35 40 45

Leu Val Ala Leu Lys Arg Ala Gln Ser Glu Leu Ala Ala His Gln

50 55 60

Lys Lys Ile Leu His Val Asp Asn His Ile Gly Ile Ser Ile Ala

65 70 75

Gly Leu Thr Ala Asp Ala Arg Leu Leu Cys Asn Phe Met Arg Gln

80 85 90

Glu Cys Leu Asp Ser Arg Phe Val Phe Asp Arg Pro Leu Pro Val

95 100 105

Ser Arg Leu Val Ser Leu Ile Gly Ser Lys Thr Gln Ile Pro Thr

110 115 120

Gln Arg Tyr Gly Arg Arg Pro Tyr Gly Val Gly Leu Leu Ile Ala

125 130 135

Gly Tyr Asp Asp Met Gly Pro His Ile Phe Gln Thr Cys Pro Ser

140 145 150

Ala Asn Tyr Phe Asp Cys Arg Ala Met Ser Ile Gly Ala Arg Ser

155 160 165

Gln Ser Ala Arg Thr Tyr Leu Glu Arg His Met Ser Glu Phe Met

170 175 180

Glu Cys Asn Leu Asn Glu Leu Val Lys His Gly Leu Arg Ala Leu

185 190 195

Arg Glu Thr Leu Pro Ala Glu Gln Asp Leu Thr Thr Lys Asn Val

200 205 210

Ser Ile Gly Ile Val Gly Lys Asp Leu Glu Phe Thr Ile Tyr Asp

215 220 225

Asp Asp Asp Val Ser Pro Phe Leu Glu Gly Leu Glu Glu Arg Pro

230 235 240

Gln Arg Lys Ala Gln Pro Ala Gln Pro Ala Asp Glu Pro Ala Glu

245 250 255

Lys Ala Asp Glu Pro Met Glu His

260

<210> 10

<211> 287

<212> PRT

<213> Homo sapiens

<400> 10

Met Lys Asp Arg Leu Ala Glu Leu Leu Asp Leu Ser Lys Gln Tyr

5 10 15

Asp Gln Gln Phe Pro Asp Gly Asp Asp Glu Phe Asp Ser Pro His

20 25 30

Glu Asp Ile Val Phe Glu Thr Asp His Ile Leu Glu Ser Leu Tyr

35 40 45

Arg Asp Ile Arg Asp Ile Gln Asp Glu Asn Gln Leu Leu Val Ala

50 55 60

Asp Val Lys Arg Leu Gly Lys Gln Asn Ala Arg Phe Leu Thr Ser

65 70 75

Met Arg Arg Leu Ser Ser Ile Lys Arg Asp Thr Asn Ser Ile Ala

80 85 90

Lys Ala Ile Lys Ala Arg Gly Glu Val Ile His Cys Lys Leu Arg

95 100 105

Ala Met Lys Glu Leu Ser Glu Ala Ala Glu Ala Gln His Gly Pro

110 115 120

His Ser Ala Val Ala Arg Ile Ser Arg Ala Gln Tyr Asn Ala Leu

125 130 135

Thr Leu Thr Phe Gln Arg Ala Met His Asp Tyr Asn Gln Ala Glu

140 145 150

Met Lys Gln Arg Asp Asn Cys Lys Ile Arg Ile Gln Arg Gln Leu

155 160 165

Glu Ile Met Gly Lys Glu Val Ser Gly Asp Gln Ile Glu Asp Met

170 175 180

Phe Glu Gln Gly Lys Trp Asp Val Phe Ser Glu Asn Leu Leu Ala

185 190 195

Asp Val Lys Gly Ala Arg Ala Ala Leu Asn Glu Ile Glu Ser Arg

200 205 210

His Arg Glu Leu Leu Arg Leu Glu Ser Arg Ile Arg Asp Val His

215 220 225

Glu Leu Phe Leu Gln Met Ala Val Leu Val Glu Lys Gln Ala Asp

230 235 240

Thr Leu Asn Val Ile Glu Leu Asn Val Gln Lys Thr Val Asp Tyr

245 250 255

Thr Gly Gln Ala Lys Ala Gln Val Arg Lys Ala Val Gln Tyr Glu

260 265 270

Glu Lys Asn Pro Cys Arg Thr Leu Cys Cys Phe Cys Cys Pro Cys

275 280 285

Leu Lys

<210> 11

<211> 244

<212> PRT

<213> Homo sapiens

<400> 11

Met Ala Ala Ala Ala Ser Pro Ala Ile Leu Pro Arg Leu Ala Ile

5 10 15

Leu Pro Tyr Leu Leu Phe Asp Trp Ser Gly Thr Gly Arg Ala Asp

20 25 30

Ala His Ser Leu Trp Tyr Asn Phe Thr Ile Ile His Leu Pro Arg

35 40 45

His Gly Gln Gln Trp Cys Glu Val Gln Ser Gln Val Asp Gln Lys

50 55 60

Asn Phe Leu Ser Tyr Asp Cys Gly Ser Asp Lys Val Leu Ser Met

65 70 75

Gly His Leu Glu Glu Gln Leu Tyr Ala Thr Asp Ala Trp Gly Lys

80 85 90

Gln Leu Glu Met Leu Arg Glu Val Gly Gln Arg Leu Arg Leu Glu

95 100 105

Leu Ala Asp Thr Glu Leu Glu Asp Phe Thr Pro Ser Gly Pro Leu

110 115 120

Thr Leu Gln Val Arg Met Ser Cys Glu Cys Glu Ala Asp Gly Tyr

125 130 135

Ile Arg Gly Ser Trp Gln Phe Ser Phe Asp Gly Arg Lys Phe Leu

140 145 150

Leu Phe Asp Ser Asn Asn Arg Lys Trp Thr Val Val His Ala Gly

155 160 165

Ala Arg Arg Met Lys Glu Lys Trp Glu Lys Asp Ser Gly Leu Thr

170 175 180

Thr Phe Phe Lys Met Val Ser Met Arg Asp Cys Lys Ser Trp Leu

185 190 195

Arg Asp Phe Leu Met His Arg Lys Lys Arg Leu Glu Pro Thr Ala

200 205 210

Pro Pro Thr Met Ala Pro Gly Leu Ala Gln Pro Lys Ala Ile Ala

215 220 225

Thr Thr Leu Ser Pro Trp Ser Phe Leu Ile Ile Leu Cys Phe Ile

230 235 240

Leu Pro Gly Ile

<210> 12

<211> 322

<212> PRT

<213> Homo sapiens

<400> 12

Met Ser Ala Lys Ser Arg Thr Ile Gly Ile Ile Gly Ala Pro Phe Ser

1 5 10 15

Lys Gly Gln Pro Arg Gly Gly Val Glu Glu Gly Pro Thr Val Leu Arg

20 25 30

Lys Ala Gly Leu Leu Glu Lys Leu Lys Glu Gln Glu Cys Asp Val Lys

35 40 45

Asp Tyr Gly Asp Leu Pro Phe Ala Asp Ile Pro Asn Asp Ser Pro Phe

50 55 60

Gln Ile Val Lys Asn Pro Arg Ser Val Gly Lys Ala Ser Glu Gln Leu

65 70 75 80

Ala Gly Lys Val Ala Glu Val Lys Lys Asn Gly Arg Ile Ser Leu Val

85 90 95

Leu Gly Gly Asp His Ser Leu Ala Ile Gly Ser Ile Ser Gly His Ala

100 105 110

Arg Val His Pro Asp Leu Gly Val Ile Trp Val Asp Ala His Thr Asp

115 120 125

Ile Asn Thr Pro Leu Thr Thr Thr Ser Gly Asn Leu His Gly Gln Pro

130 135 140

Val Ser Phe Leu Leu Lys Glu Leu Lys Gly Lys Ile Pro Asp Val Pro

145 150 155 160

Gly Phe Ser Trp Val Thr Pro Cys Ile Ser Ala Lys Asp Ile Val Tyr

165 170 175

Ile Gly Leu Arg Asp Val Asp Pro Gly Glu His Tyr Ile Leu Lys Thr

180 185 190

Leu Gly Ile Lys Tyr Phe Ser Met Thr Glu Val Asp Arg Leu Gly Ile

195 200 205

Gly Lys Val Met Glu Glu Thr Leu Ser Tyr Leu Leu Gly Arg Lys Lys

210 215 220

Arg Pro Ile His Leu Ser Phe Asp Val Asp Gly Leu Asp Pro Ser Phe

225 230 235 240

Thr Pro Ala Thr Gly Thr Pro Val Val Gly Gly Leu Thr Tyr Arg Glu

245 250 255

Gly Leu Tyr Ile Thr Glu Glu Ile Tyr Lys Thr Gly Leu Leu Ser Gly

260 265 270

Leu Asp Ile Met Glu Val Asn Pro Ser Leu Gly Lys Thr Pro Glu Glu

275 280 285

Val Thr Arg Thr Val Asn Thr Ala Val Ala Ile Thr Leu Ala Cys Phe

290 295 300

Gly Leu Ala Arg Glu Gly Asn His Lys Pro Ile Asp Tyr Leu Asn Pro

305 310 315 320

Pro Lys

<210> 13

<211> 213

<212> PRT

<213> Homo sapiens

<400> 13

Met Asp Thr Glu Arg Val Gly Asp Gly Lys Gln His Arg Arg Lys Gln

1 5 10 15

Ser Gln Arg Leu Arg Trp Pro Cys Cys Leu Ala Leu Val Pro Asp Arg

20 25 30

His Pro Ser Gln Leu Ser Ser Cys Thr Leu Cys Leu Leu Ala Ala Ala

35 40 45

Ser Gln Trp Glu Ser Trp Ala His Phe Ser Lys Trp His Thr Glu Ala

50 55 60

Ser Thr Gly Thr His Leu Gly Lys Ala Lys Ala Glu Gly Leu Gly Cys

65 70 75 80

Thr Val Lys Asn Thr Pro Gln Leu Ser Ile Cys Glu Pro Ser His Phe

85 90 95

Gly Pro Ser Phe Val His Ser Pro His Leu Leu Val Asp His Asp His

100 105 110

Arg Ala Gly Ala Ala Thr Gly Arg Gly Gln Ala Gly Pro Ser Arg Ala

115 120 125

Ser Ser Val Ser Pro Thr Phe Cys Pro Pro Thr Thr Ser His His Pro

130 135 140

Val Cys Ala Lys Gly Thr Asp Pro Val Leu Val Leu Gln Glu Glu Glu

145 150 155 160

Gln Asp Leu Asp Gly Glu Lys Gly Pro Ser Ser Glu Gly Pro Glu Glu

165 170 175

Glu Asp Gly Glu Gly Phe Ser Phe Lys Tyr Ser Pro Gly Lys Leu Arg

180 185 190

Gly Asn Gln Tyr Lys Lys Met Met Thr Lys Glu Glu Leu Glu Glu Glu

195 200 205

Gln Arg Thr Glu Glu

210

<210> 14

<211> 117

<212> PRT

<213> Homo sapiens

<400> 14

Gly Glu Ala Arg Gly Lys Leu Leu Gln Leu Ile Glu Gln Gln Lys Leu

1 5 10 15

Val Gly Leu Asn Leu Ser Pro Pro Met Ser Pro Val Gln Leu Pro Leu

20 25 30

Arg Ala Trp Thr Glu Gly Ala Lys Arg Thr Ile Glu Val Ser Ile Pro

35 40 45

Gly Ala Glu Ala Pro Glu Ser Ser Lys Cys Ser Thr Val Ser Pro Val

50 55 60

Ser Gly Ile Asn Thr Arg Arg Ser Ser Gly Ala Thr Gly Asn Ser Cys

65 70 75 80

Ser Pro Leu Asn Ala Thr Ser Gly Ser Gly Arg Phe Thr Pro Leu Asn

85 90 95

Pro Arg Ala Lys Ile Glu Lys Gln Asn Glu Glu Gly Trp Phe Ala Leu

100 105 110

Ser Thr His Val Ser

115

<210> 15

<211> 1046

<212> PRT

<213> Homo sapiens

<400> 15

Arg Gln Gly Gly Arg Pro Ser Ser Pro Gln Ala Ser Arg Ala Arg Gln

1 5 10 15

Leu Pro Ser Ile Glu Ile Gln Gln Trp Glu Gln Asn Leu Glu Lys Phe

20 25 30

His Met Asp Leu Phe Arg Met Arg Cys Tyr Leu Ala Ser Leu Gln Gly

35 40 45

Gly Glu Leu Pro Asn Pro Lys Ser Leu Leu Ala Ala Ala Ser Arg Pro

50 55 60

Ser Lys Leu Ala Leu Gly Arg Leu Gly Ile Leu Ser Val Ser Ser Phe

65 70 75 80

His Ala Leu Val Cys Ser Arg Asp Asp Ser Ala Leu Arg Lys Arg Thr

85 90 95

Leu Ser Leu Thr Gln Arg Gly Arg Asn Lys Lys Gly Ile Phe Ser Ser

100 105 110

Leu Lys Gly Leu Asp Thr Leu Ala Arg Lys Gly Lys Glu Lys Arg Pro

115 120 125

Ser Ile Thr Gln Val Asp Glu Leu Leu His Ile Tyr Gly Ser Thr Val

130 135 140

Asp Gly Val Pro Arg Asp Asn Ala Trp Glu Ile Gln Thr Tyr Val His

145 150 155 160

Phe Gln Asp Asn His Gly Val Thr Val Gly Ile Lys Pro Glu His Arg

165 170 175

Val Glu Asp Ile Leu Thr Leu Ala Cys Lys Met Arg Gln Leu Glu Pro

180 185 190

Ser His Tyr Gly Leu Gln Leu Arg Lys Leu Val Asp Asp Asn Val Glu

195 200 205

Tyr Cys Ile Pro Ala Pro Tyr Glu Tyr Met Gln Gln Gln Val Tyr Asp

210 215 220

Glu Ile Glu Val Phe Pro Leu Asn Val Tyr Asp Val Gln Leu Thr Lys

225 230 235 240

Thr Gly Ser Val Cys Asp Phe Gly Phe Ala Val Thr Ala Gln Val Asp

245 250 255

Glu Arg Gln His Leu Ser Arg Ile Phe Ile Ser Asp Val Leu Pro Asp

260 265 270

Gly Leu Ala Tyr Gly Glu Gly Leu Arg Lys Gly Asn Glu Ile Met Thr

275 280 285

Leu Asn Gly Glu Ala Val Ser Asp Leu Asp Leu Lys Gln Met Glu Ala

290 295 300

Leu Phe Ser Glu Lys Ser Val Gly Leu Thr Leu Ile Ala Arg Pro Pro

305 310 315 320

Asp Thr Lys Ala Thr Leu Cys Thr Ser Trp Ser Asp Ser Asp Leu Phe

325 330 335

Ser Arg Asp Gln Lys Ser Leu Leu Pro Pro Pro Asn Gln Ser Gln Leu

340 345 350

Leu Glu Glu Phe Leu Asp Asn Phe Lys Lys Asn Thr Ala Asn Asp Phe

355 360 365

Ser Asn Val Pro Asp Ile Thr Thr Gly Leu Lys Arg Ser Gln Thr Asp

370 375 380

Gly Thr Leu Asp Gln Val Ser His Arg Glu Lys Met Glu Gln Thr Phe

385 390 395 400

Arg Ser Ala Glu Gln Ile Thr Ala Leu Cys Arg Ser Phe Asn Asp Ser

405 410 415

Gln Ala Asn Gly Met Glu Gly Pro Arg Glu Asn Gln Asp Pro Pro Pro

420 425 430

Arg Ser Leu Ala Arg His Leu Ser Asp Ala Asp Arg Leu Arg Lys Val

435 440 445

Ile Gln Glu Leu Val Asp Thr Glu Lys Ser Tyr Val Lys Asp Leu Ser

450 455 460

Cys Leu Phe Glu Leu Tyr Leu Glu Pro Leu Gln Asn Glu Thr Phe Leu

465 470 475 480

Thr Gln Asp Glu Met Glu Ser Leu Phe Gly Ser Leu Pro Glu Met Leu

485 490 495

Glu Phe Gln Lys Val Phe Leu Glu Thr Leu Glu Asp Gly Ile Ser Ala

500 505 510

Ser Ser Asp Phe Asn Thr Leu Glu Thr Pro Ser Gln Phe Arg Lys Leu

515 520 525

Leu Phe Ser Leu Gly Gly Ser Phe Leu Tyr Tyr Ala Asp His Phe Lys

530 535 540

Leu Tyr Ser Gly Phe Cys Ala Asn His Ile Lys Val Gln Lys Val Leu

545 550 555 560

Glu Arg Ala Lys Thr Asp Lys Ala Phe Lys Ala Phe Leu Asp Ala Arg

565 570 575

Asn Pro Thr Lys Gln His Ser Ser Thr Leu Glu Ser Tyr Leu Ile Lys

580 585 590

Pro Val Gln Arg Val Leu Lys Tyr Pro Leu Leu Leu Lys Glu Leu Val

595 600 605

Ser Leu Thr Asp Gln Glu Ser Glu Glu His Tyr His Leu Thr Glu Ala

610 615 620

Leu Lys Ala Met Glu Lys Val Ala Ser His Ile Asn Glu Met Gln Lys

625 630 635 640

Ile Tyr Glu Asp Tyr Gly Thr Val Phe Asp Gln Leu Val Ala Glu Gln

645 650 655

Ser Gly Thr Glu Lys Glu Val Thr Glu Leu Ser Met Gly Glu Leu Leu

660 665 670

Met His Ser Thr Val Ser Trp Leu Asn Pro Phe Leu Ser Leu Gly Lys

675 680 685

Ala Arg Lys Asp Leu Glu Leu Thr Val Phe Val Phe Lys Arg Ala Val

690 695 700

Ile Leu Val Tyr Lys Glu Asn Cys Lys Leu Lys Lys Lys Leu Pro Ser

705 710 715 720

Asn Ser Arg Pro Ala His Asn Ser Thr Asp Leu Asp Pro Phe Lys Phe

725 730 735

Arg Trp Leu Ile Pro Ile Ser Ala Leu Gln Val Arg Leu Gly Asn Pro

740 745 750

Ala Gly Thr Glu Asn Asn Ser Ile Trp Glu Leu Ile His Thr Lys Ser

755 760 765

Glu Ile Glu Gly Arg Pro Glu Thr Ile Phe Gln Leu Cys Cys Ser Asp

770 775 780

Ser Glu Ser Lys Thr Asn Ile Val Lys Val Ile Arg Ser Ile Leu Arg

785 790 795 800

Glu Asn Phe Arg Arg His Ile Lys Cys Glu Leu Pro Leu Glu Lys Thr

805 810 815

Cys Lys Asp Arg Leu Val Pro Leu Lys Asn Arg Val Pro Val Ser Ala

820 825 830

Lys Leu Ala Ser Ser Arg Ser Leu Lys Val Leu Lys Asn Ser Ser Ser

835 840 845

Asn Glu Trp Thr Gly Glu Thr Gly Lys Gly Thr Leu Leu Asp Ser Asp

850 855 860

Glu Gly Ser Leu Ser Ser Gly Thr Gln Ser Ser Gly Cys Pro Thr Ala

865 870 875 880

Glu Gly Arg Gln Asp Ser Lys Ser Thr Ser Pro Gly Lys Tyr Pro His

885 890 895

Pro Gly Leu Ala Asp Phe Ala Asp Asn Leu Ile Lys Glu Ser Asp Ile

900 905 910

Leu Ser Asp Glu Asp Asp Asp His Arg Gln Thr Val Lys Gln Gly Ser

915 920 925

Pro Thr Lys Asp Ile Glu Ile Gln Phe Gln Arg Leu Arg Ile Ser Glu

930 935 940

Asp Pro Asp Val His Pro Glu Ala Glu Gln Gln Pro Gly Pro Glu Ser

945 950 955 960

Gly Glu Gly Gln Lys Gly Gly Glu Gln Pro Lys Leu Val Arg Gly His

965 970 975

Phe Cys Pro Ile Lys Arg Lys Ala Asn Ser Thr Lys Arg Asp Arg Gly

980 985 990

Thr Leu Leu Lys Ala Gln Ile Arg His Gln Ser Leu Asp Ser Gln Ser

995 1000 1005

Glu Asn Ala Thr Ile Asp Leu Asn Ser Val Leu Glu Arg Glu Phe

1010 1015 1020

Ser Val Gln Ser Leu Thr Ser Val Val Ser Glu Glu Cys Phe Tyr

1025 1030 1035

Glu Thr Glu Ser His Gly Lys Ser

1040 1045

<210> 16

<211> 232

<212> PRT

<213> Homo sapiens

<400> 16

Met Glu Asp Leu Glu Glu Asp Val Arg Phe Ile Val Asp Glu Thr Leu

1 5 10 15

Asp Phe Gly Gly Leu Ser Pro Ser Asp Ser Arg Glu Glu Glu Asp Ile

20 25 30

Thr Val Leu Val Thr Pro Glu Lys Pro Leu Arg Arg Gly Leu Ser His

35 40 45

Arg Ser Asp Pro Asn Ala Val Ala Pro Ala Pro Gln Gly Val Arg Leu

50 55 60

Ser Leu Gly Pro Leu Ser Pro Glu Lys Leu Glu Glu Ile Leu Asp Glu

65 70 75 80

Ala Asn Arg Leu Ala Ala Gln Leu Glu Gln Cys Ala Leu Gln Asp Arg

85 90 95

Glu Ser Ala Gly Glu Gly Leu Gly Pro Arg Arg Val Lys Pro Ser Pro

100 105 110

Arg Arg Glu Thr Phe Val Leu Lys Asp Ser Pro Val Arg Asp Leu Leu

115 120 125

Pro Thr Val Asn Ser Leu Thr Arg Ser Thr Pro Ser Pro Ser Ser Leu

130 135 140

Thr Pro Arg Leu Arg Ser Asn Asp Arg Lys Gly Ser Val Arg Ala Leu

145 150 155 160

Arg Ala Thr Ser Gly Lys Arg Pro Ser Asn Met Lys Arg Glu Ser Pro

165 170 175

Thr Cys Asn Leu Phe Pro Ala Ser Lys Ser Pro Ala Ser Ser Pro Leu

180 185 190

Thr Arg Ser Thr Pro Pro Val Arg Gly Arg Ala Gly Pro Ser Gly Arg

195 200 205

Ala Ala Ala Ser Pro Pro Thr Pro Ile Arg Ser Val Leu Ala Pro Gln

210 215 220

Pro Ser Thr Ser Asn Ser Gln Arg

225 230

<210> 17

<211> 63

<212> PRT

<213> Homo sapiens

<400> 17

Met Leu Gly Gln Ser Ile Arg Arg Phe Thr Thr Ser Val Val Arg Arg

1 5 10 15

Ser His Tyr Glu Glu Gly Pro Gly Lys Asn Leu Pro Phe Ser Val Glu

20 25 30

Asn Lys Trp Ser Leu Leu Ala Lys Met Cys Leu Tyr Phe Gly Ser Ala

35 40 45

Phe Ala Thr Pro Phe Leu Val Val Arg His Gln Leu Leu Lys Thr

50 55 60

Claims (8)

1. A tool for determining whether the levels of at least one set of biomarkers in a blood sample of a subject are abnormal, for the purpose of manufacturing products for the physiological characterization of a subject, said set of biomarkers comprising the following biomarkers: interleukin (IL)-2, IL-5, IL-8, IL-10, MIF, matrix metalloproteinase 7 (MMP-7), MMP-9, resistin, SAA, and myeloperoxidase (MPO), wherein abnormal levels of said biomarkers indicate non-small cell lung cancer. 2. The use as described in claim 1, wherein the subject is male. 3. The use as described in claim 1, wherein the subject is female. 4. The use as described in claim 1, wherein determining the level of the biomarker includes performing a quantitative multiplex immunoassay. 5. The use as described in claim 1, wherein the blood sample is serum or plasma. 6. The use as described in claim 1, wherein the subject is a mammal. 7. The use as described in claim 1, wherein the biomarker group consists of no more than 20 biomarkers. 8. The use as described in claim 1, wherein the biomarker group consists of no more than 10 biomarkers.